Kinesthetic interactions in museums: conveying cultural heritage by making use of ancient tools and (re-) constructing artworks

Kinesthetic interactions allow users to interact with 3D applications through their body movements and hand gestures. When kinesthetic applications are introduced in museums and heritage institutions, they add embodiment to visitor experience. An appropriate fit for kinesthetic technology in museums rests on visitors engaging in purposeful body movements and hand gestures that convey meanings about both intangible and tangible heritage. This paper presents the design, development and evaluation of a kinesthetic application of sculpturing Cycladic figurines, which places the user at the role of an ancient craftsman who creates a figurine with bare-hand movements (translated by Leap Motion to respective sculpting actions) in a simplified virtual environment. The Cycladic sculpture application has been evaluated in laboratory and field testing (as part of a wider educational activity in the museum) with positive results on usability, fun and learning. We identify several benefits as well as challenges of designing kinesthetic interactions in museums and we report on design issues that need to be taken into account in similar applications.     

A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes

Outlier detection has attracted substantial attention in many applications and research areas; some of the most prominent applications are network intrusion detection or credit card fraud detection. Many of the existing approaches are based on calculating distances among the points in the dataset. These approaches cannot easily adapt to current datasets that usually contain a mix of categorical and continuous attributes, and may be distributed among different geographical locations. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose a fast distributed outlier detection strategy intended for datasets containing mixed attributes. The proposed method takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and the number of attributes in the dataset. Experimental results show that the proposed outlier detection method compares very favorably with other state-of-the art outlier detection strategies proposed in the literature and that the speedup achieved by its distributed version is very close to linear.     

Scanning scene tunnel for city traversing

This paper proposes a visual representation named scene tunnel for capturing urban scenes along routes and visualizing them on the Internet. We scan scenes with multiple cameras or a fish-eye camera on a moving vehicle, which generates a real scene archive along streets that is more complete than previously proposed route panoramas. Using a translating spherical eye, properly set planes of scanning, and unique parallel-central projection, we explore the image acquisition of the scene tunnel from camera selection and alignment, slit calculation, scene scanning, to image integration. The scene tunnels cover high buildings, ground, and various viewing directions and have uniformed resolutions along the street. The sequentially organized scene tunnel benefits texture mapping onto the urban models. We analyze the shape characteristics in the scene tunnels for designing visualization algorithms. After combining this with a global panorama and forward image caps, the capped scene tunnels can provide continuous views directly for virtual or real navigation in a city. We render scene tunnel dynamically by view warping, fast transmission, and flexible interaction. The compact and continuous scene tunnel facilitates model construction, data streaming, and seamless route traversing on the Internet and mobile devices.     

Audio recording of rock/metal music using personal computers

Personal computers and their relevant technologies have been widely used by artists and musicians to create and record their own music and electroacoustic compositions. ＂Laptoppers＂ are famous for using their laptops for their dance/electronic beats and music. A genre that has not relied on the use of PCs for the production of its music is rock/heavy metal, since bands of these genres usually book recording studio time where professionals take on the task of the production using expensive equipment. This study shows that in today＇s day and age, and with the software and hardware currently available, it is possible for rock/metal artists to use their PC to record and produce their own CD successfully and at an extremely competitive cost. The effort＇s of a rock band that does just this is followed from the beginning and the results of their CD production and song successes is presented. The article also serves as a ＂HowTo＂ guide that bands on a low budget can follow to make good quality demo CDs and enter the music business industry.     

On a finite element method for dynamic contact/impact problems

This paper addresses the formulation and discrete approximation of dynamic contact/impact initial-value problems. The continuous problem is presented in the context of non-linear kinematics. Standard semi-discrete time integrators are introduced and are shown to be unsuccessful in modelling the kinematic constraints imposed on the interacting bodies during persistent contact. A procedure that bypasses the aforementioned difficulty is proposed by means of a novel variational formulation. Numerical simulations are conducted and the results are reported and discussed.     

Bedload Transport in Gravel-Bed Streams with Unimodal Sediment

Bedload transport in many gravel-bed streams becomes highly complicated because of the nonuniformity of the grain size and the vertical stratification of channel bed material. A new relation for computing bedload transport rates in gravel-bed streams is proposed here. In an effort to account for the variation of the makeup of the surface material within a wide range of Shields stresses, the relation employs a two-parameter approach, one related to the material in the pavement and the other to that in the subpavement layers. The mode is used to represent the grain sizes of each layer. Available bedload transport data from gravel-bed streams with unimodal sediment are used to test the accuracy of the relation. A comparison with other bedload transport relations is also considered.     

Classification of noisy signals using fuzzy ARTMAP neural networks

This paper describes an approach to classification of noisy signals using a technique based on the fuzzy ARTMAP neural network (FAMNN). The proposed method is a modification of the testing phase of the fuzzy ARTMAP that exhibits superior generalization performance compared to the generalization performance of the standard fuzzy ARTMAP in the presence of noise. An application to textured gray-scale image segmentation is presented. The superiority of the proposed modification over the standard fuzzy ARTMAP is established by a number of experiments using various texture sets, feature vectors and noise types. The texture sets include various aerial photos and also samples obtained from the Brodatz album. Furthermore, the classification performance of the standard and the modified fuzzy ARTMAP is compared for different network sizes. Classification results that illustrate the performance of the modified algorithm and the FAMNN are presented.     

A Data‐Driven Framework for Visual Crowd Analysis

We present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.     

Approach to Separate Sand from Gravel for Bed-Load Transport Calculations in Streams with Bimodal Sediment

The bed material found in gravel-bed streams is nonuniform in terms of grain size and can typically be classified as unimodal or bimodal. The latter type of sediment distribution is usually represented by two modes, one of sand size and another of gravel. For this case, the movement of one mode becomes nonlinearly influenced by the other. As a result, the presence of the two modes in a bimodal material complicates the calculation of bed-load transport rates. The present study proposes an approach to separate the calculation of bed-load transport rates for bimodal materials into two independent fractions of sand and gravel, thereby rendering the bed sediment into two unimodal components. This approach is accomplished by decoupling the two fractions through scaling the reference Shields stresses of the sand and gravel modes to match the value of the mode of unimodal materials. Consequently, the contribution of each fraction to bed load can be estimated using a suitable relation derived for unimodal materials. Laboratory and field bed-load data available in the literature are used to examine the validity of the overall approach.