There is an increasing interest in creating pervasive games based on emerging interaction technologies. In order to develop touch-less, interactive and augmented reality games on vision-based wearable device, a touch-less motion interaction technology is designed and evaluated in this work. Users interact with the augmented reality games with dynamic hands/feet gestures in front of the camera, which triggers the interaction event to interact with the virtual object in the scene. Three primitive augmented reality games with eleven dynamic gestures are developed based on the proposed touch-less interaction technology as proof. At last, a comparing evaluation is proposed to demonstrate the social acceptability and usability of the touch-less approach, running on a hybrid wearable framework or with Google Glass, as well as workload assessment, user's emotions and satisfaction.

Touch-less interactive augmented reality game on vision-based wearable device

This is the preprint version of our paper on Personal and Ubiquitous Computing. There is an increasing interest in creating pervasive games based on emerging interaction technologies. In order to develop touch-less, interactive and augmented reality games on vision-based wearable device, a touch-less motion interaction technology is designed and evaluated in this work. Users interact with the augmented reality games with dynamic hands/feet gestures in front of the camera, which triggers the interaction event to interact with the virtual object in the scene. Three primitive augmented reality games with eleven dynamic gestures are developed based on the proposed touch-less interaction technology as proof. At last, a comparing evaluation is proposed to demonstrate the social acceptability and usability of the touch-less approach, running on a hybrid wearable framework or with Google Glass, as well as workload assessment, user's emotions and satisfaction.

Preprint Touch-less Interactive Augmented Reality Game on Vision Based Wearable Device

Web traffic refers to the amount of data that is sent and received by people visiting online websites. Web traffic anomalies represent abnormal changes in time series traffic, and it is important to perform detection quickly and accurately for the efficient operation of complex computer networks systems. In this paper, we propose a C-LSTM neural network for effectively modeling the spatial and temporal information contained in traffic data, which is a one-dimensional time series signal. We also provide a method for automatically extracting robust features of spatial-temporal information from raw data. Experiments demonstrate that our C-LSTM method can extract more complex features by combining a convolutional neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN). The CNN layer is used to reduce the frequency variation in spatial information; the LSTM layer is suitable for modeling time information; and the DNN layer is used to map data into a more separable space. Our C-LSTM method also achieves nearly perfect anomaly detection performance for web traffic data, even for very similar signals that were previously considered to be very difficult to classify. Finally, the C-LSTM method outperforms other state-of-the-art machine learning techniques on Yahoo's well-known Webscope S5 dataset, achieving an overall accuracy of 98.6% and recall of 89.7% on the test dataset.

Web traffic anomaly detection using C-LSTM neural networks

Distributed denial-of-service is one kind of the most highlighted and most important attacks of today’s cyberworld. With simple but extremely powerful attack mechanisms, it introduces an immense th...

A survey of distributed denial-of-service attack, prevention, and mitigation techniques:

The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems—toed-in camera configuration and parallel camera configuration—are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.

https://www.mdpi.com/1424-8220/15/8/20925/pdf

A Self-Assessment Stereo Capture Model Applicable to the Internet of Things.

A transform domain-based anomaly detection approach to network-wide traffic

Abnormal network traffic has an important impact on network activities and leads to the severe damage to our networks because they are usually related with network faults and network attacks. How to detect effectively network traffic anomalies is an open issue for network researchers. This paper proposes a novel method for detecting traffic anomalies in high-speed backbone networks, based on multi-scale analysis. Firstly, the continuous wavelet transforms are performed for network traffic in multiple continuous scales. We then use the principal component analysis for the continuous wavelet transforms in the different scales and extract the nature of the anomalous network traffic. And the new mapping function is constructed to detect the abnormal traffic. Finally, we use the traffic data from the real network to validate our method. Simulation results show that our approach is more promising than the previous method.Copyright © 2013 John Wiley & Sons, Ltd.

Multi-scale anomaly detection for high-speed network traffic

Anomaly traffic often breaks out without any omen and brings a breakdown to networks in a short time, and thus the adaptive detection of anomalies in network traffic is an important and challenging task. In this paper, we propose a wavelet-based adaptive approach to detect anomalies in network traffic. We can use wavelet packet transform and continuous wavelet transform to perform the adaptive detect anomaly. First, wavelet packet transform is exploited to extract the anomaly characteristics on the different scales. Then continue wavelet transform is exploited to obtain the further anomaly information about network traffic. Simulation results show that our method is effective and feasible.

Zhengzheng Xu

Papers

A transform domain-based anomaly detection approach to network-wide traffic

Multi-scale anomaly detection for high-speed network traffic

A Wavelet-Based Detection Approach to Traffic Anomalies