Reliability and validity of self-reported assessment of exposure and outcome variables for manual lifting tasks: a preliminary investigation

The reliability and validity of self-reported assessment of exposure and outcome variables were examined for manual lifting activities among ten physiotherapists. In this study, the participants evaluated the effects of five lifting variables on perceived effort, twice separated by a one-week period. One hundred and sixty-two lifting conditions were evaluated by each subject. The exposure and outcome lifting variables were described in linguistic terms. Intra-class correlation coefficient (ICC(1,1)) analysis revealed a mean value of 0.62 for all lifting activities. The self-reported assessment was cross-validated with the NIOSH lifting index by mapping the linguistic variables into numerical ranges. Moderate correlations (r = 0.54 and 0.53, p<0.01) were obtained between perceived physical exertion/perceived risk and lifting index. The findings of this study provide preliminary indications that human-based methodologies may be further explored on experienced workers.     

COMPLEXITY RELAXATION OF THE TENSOR PRODUCT MODEL TRANSFORMATION FOR HIGHER DIMENSIONAL PROBLEMS

The Tensor Product (TP) model transformation method was proposed recently as an automated gateway between a class of non‐linear models and linear matrix inequality based control design. The core of the TP model transformation is the higher order singular value decomposition of a large sized tensor, which requires high computational power that is usually outside of a regular computer capacity in cases of higher dimensionality. This disadvantage restricts the utilization of the TP model transformation to models having smaller dimensionality. The aim of this paper is to propose a computationally relaxed version of the TP model transformation. The paper also presents a 6 dimensional example to show the effectiveness of the modified transformation.     

Interleaved Traffic Splitting: A promising technique to solve False Timeout

In this paper, we analyze the False Timeout (FTO) problem that TCP flows suffered in OBS networks and propose Interleaved Traffic Splitting (ITS) to solve the problem. We show that the collision loss of ACK bursts may also cause FTO, which has been neglected by previous researchers. As TCP Sack is designed to recover from multiple packet losses, we find that a Sack flow can recover its data transmission without triggering retransmission timeout if packet loss is not too severe. This suggests limiting the number of packets carried by a single burst. In ITS, this is achieved by carrying out a parallel, interleaved burst assembly process at each ingress OBS edge router. We show that ITS tackles the FTO problem caused by the loss of both data and ACK bursts. Unlike other existing approaches, the ITS scheme requires only modification to the assembly/de-assembly mechanisms implemented at the edge routers. It does not add complexities to the network management or the router design. In this paper, both analytical and simulation results are presented to demonstrate the performance improvement of ITS. Our analysis further shows that the use of two parallel bursts is optimum for ITS.     

Computation of madalines' sensitivity to input and weight perturbations

The sensitivity of a neural network's output to its input and weight perturbations is an important measure for evaluating the network's performance. In this letter, we propose an approach to quantify the sensitivity of Madalines. The sensitivity is defined as the probability of output deviation due to input and weight perturbations with respect to overall input patterns. Based on the structural characteristics of Madalines, a bottom-up strategy is followed, along which the sensitivity of single neurons, that is, Adalines, is considered first and then the sensitivity of the entire Madaline network. By means of probability theory, an analytical formula is derived for the calculation of Adalines' sensitivity, and an algorithm is designed for the computation of Madalines' sensitivity. Computer simulations are run to verify the effectiveness of the formula and algorithm. The simulation results are in good agreement with the theoretical results.     

CPE: a parallel library for financial engineering applications

The Clustertech parallel environment is an object-oriented C++ library that uses abstractions to simplify parallel programming for financial engineering applications. The message passing interface ensures CPE's portability and performance over a wide range of parallel cluster and symmetric multiprocessing machines.     

Copy move forgery detection based on keypoint and patch match

Multimedia Tools and Applications - Copy move has become a simple and effective operation for image forgeries due to the advancement of image editing software, which is still challenging to be...     

Robust path-based spectral clustering

Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data. In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings. Our proposed method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both synthetic and real-world data, comparing our method with some other methods. In particular, color images from the Berkeley segmentation data set and benchmark are used in the image segmentation experiments. Experimental results show that our method consistently outperforms other methods due to its higher robustness.     

Designing Efficient and Robust Caching Algorithms for Streaming-on-Demand Services on the Internet

Content Delivery Networks (CDN) have been used on the Internet to cache media content so as to reduce the load on the original media server, network congestion, and latency. Due to the large size of media content compared to normal web objects, current caching algorithms used in the Internet are no longer suitable. This paper presents a high-performance prefetch system that accommodates user time-varying behavior. A hybrid caching technique, which combines prefetch and replacement algorithms, is also introduced. The robustness of the cache system against imperfect user request information is evaluated using three request noise models. Two prefetch performance indices are also presented to help content administrators in deciding when to update the user request profile for caching algorithms.     

Use of bias term in projection pursuit learning improvesapproximation and convergence properties

In a regression problem, one is given a multidimensional random vector X, the components of which are called predictor variables, and a random variable, Y, called response. A regression surface describes a general relationship between X and Y. A nonparametric regression technique that has been successfully applied to high-dimensional data is projection pursuit regression (PPR). The regression surface is approximated by a sum of empirically determined univariate functions of linear combinations of the predictors. Projection pursuit learning (PPL) formulates PPR using a 2-layer feedforward neural network. The smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite functions of some predefined highest order R. We demonstrate that PPL networks in the original form do not have the universal approximation property for any finite R, and thus cannot converge to the desired function even with an arbitrarily large number of hidden units. But, by including a bias term in each linear projection of the predictor variables, PPL networks can regain these capabilities, independent of the exact choice of R. Experimentally, it is shown in this paper that this modification increases the rate of convergence with respect to the number of hidden units, improves the generalization performance, and makes it less sensitive to the setting of R. Finally, we apply PPL to chaotic time series prediction, and obtain superior results compared with the cascade-correlation architecture.     

Evaluation of physiological work demands and low back neuromuscular fatigue on nurses working in geriatric wards

This study evaluated the physiological demands and low back neuromuscular fatigue patterns following a daytime work shift of 21 female nurses working in geriatric wards. Subjects' heart rate (HR) at work was monitored for 8 h and surface electromyogram (EMG) of their back muscle was recorded during a 1-minute horizontal trunk holding test before and after work. Results showed that the nurses had heart rates exceeding 90 beats/min in 57% and 110 beats/min in 19% of their working hours. The EMG revealed a significant drop in initial median frequency (MF) (p = 0.04) and increase in negative slope of the MF/time plot (p<0.001) of the back muscle after work. These suggest that the muscles were fatigued after work. The nurses rated patient lifting, transfer and turning as most physically demanding, and these corresponded to the highest heart rates recorded. The physiological and neuromuscular strain on the nurses indicated that extreme care should be exercised at work, particularly towards the end of a work shift when the muscles have become fatigued.