Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns

Efficient Client-to-Server Assignments for Distributed Virtual Environments

Distributed Virtual Environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign the participating clients to servers to enhance users ’ experience in interacting within the DVE. This problem is termed the client assignment problem. In this paper, we propose a two-phase approach, consisting of an initial assignment phase and a refined assignment phase to address this problem. Both phases are shown to be NP-hard, and several heuristic assignment algorithms are then devised based on this two-phase approach. Via extensive simulation studies with realistic settings, we evaluate these algorithms in terms of their performances in enhancing interactivity of the DVE. 1

Re-examination of chinese semantic processing and syntactic processing: Evidence from conventional ERPs and reconstructed ERPs by residue iteration decomposition (RIDE)

© 2015 Wang et al. A number of studies have explored the time course of Chinese semantic and syntactic processing. However, whether syntactic processing occurs earlier than semantics during Chinese sentence reading is still under debate. To further explore this issue, an event-related potentials (ERPs) experiment was conducted on 21 native Chinese speakers who read individually-presented Chinese simple sentences (NP1+VP+NP2) word-by-word for comprehension and made semantic plausibility judgments. The transitivity of the verbs was manipulated to form three types of stimuli: congruent sentences (CON), sentences with a semantically violated NP2 following a transitive verb (semantic violation, SEM), and sentences with a semantically violated NP2 following an intransitive verb (combined semantic and syntactic violation, SEM+SYN). The ERPs evoked from the target NP2 were analyzed by using the Residue Iteration Decomposition (RIDE) method to reconstruct the ERP waveform blurred by trial-to-trial variability, as well as by using the conventional ERP method based on stimulus-locked averaging. The conventional ERP analysis showed that, compared with the critical words in CON, those in SEM and SEM+SYN elicited an N400-P600 biphasic pattern. The N400 effects in both violation conditions were of similar size and distribution, but the P600 in SEM+SYN was bigger than that in SEM. Compared with the conventional ERP analysis, RIDE analysis revealed a larger N400 effect and an earlier P600 effect (in the time window of 500-800 ms instead of 570-810ms). Overall, the combination of conventional ERP analysis and the RIDE method for compensating for trial-to-trial variability confirmed the non-significant difference between SEM and SEM+SYN in the earlier N400 time window. Converging with previous findings on other Chinese structures, the currentstudy provides further precise evidence that syntactic processing in Chinese does not occur earlier than semantic processing.Link_to_subscribed_fulltex

A review of interactive narrative systems and technologies: a training perspective

As an emerging form of digital entertainment, interactive narrative has attracted great attention of researchers over the past decade. Recently, there is an emerging trend to apply interactive narrative for training and simulation. An interactive narrative system allows players to proactively interact with simulated entities in a virtual world and have the ability to alter the progression of a storyline. In simulation-based training, the use of an interactive narrative system enables the possibility to offer engaging, diverse and personalized narratives or scenarios for different training purposes. This paper provides a review of interactive narrative systems and technologies from a training perspective. Specifically, we first propose a set of key requirements in developing interactive narrative systems for simulation-based training. Then we review nine representative existing systems with respect to their system architectures, features and related mechanisms. To examine their applicability to training, we investigate and compare the reviewed systems based on the functionalities and modules that support the proposed requirements. Furthermore, we discuss some open research issues on future development of interactive narrative technologies for training applications

RA2: predicting simulation execution time for cloud-based design space explorations

Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds

High-dimensional Objective-based Data Farming

In objective-based data farming, decision variables of the Red Team are evolved using evolutionary algorithms such that a series of rigorous Red Team strategies can be generated to assess the Blue Team\u27s operational tactics. Typically, less than 10 decision variables (out of 1000+) are selected by subject matter experts (SMEs) based on their past experience and intuition. While this approach can significantly improve the computing efficiency of the data farming process, it limits the chance of discovering “surprises” and moreover, data farming may be used only to verify SMEs\u27 assumptions. A straightforward solution is simply to evolve all Red Team parameters without any SME involvement. This modification significantly increases the search space and therefore we refer to it as high-dimensional objective-based data farming (HD-OBDF). The potential benefits of HD-OBDF include: possible better performance and information about more important decision variables. In this paper, several state-of-the-art multi-objective evolutionary algorithms are applied in HD-OBDF to assess their suitability in terms of convergence speed and Pareto efficiency. Following that, we propose two approaches to identify dominant/key evolvable parameters in HD-OBDF - decision variable coverage and diversity spread