A new dual wing harmonium model for document retrieval

A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.     

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks

Personal mobility carbon allowance (PMCA) schemes are designed to reduce carbon consumption from transportation networks. PMCA schemes influence the travel decision process of users and accordingly impact the system metrics including travel time and greenhouse gas (GHG) emissions. We develop a multi-user class dynamic user equilibrium model to evaluate the transportation system performance when PMCA scheme is implemented. The results using Sioux-Falls test network indicate that PMCA schemes can achieve the emissions reduction goals for transportation networks. Further, users characterized by high value of travel time are found to be less sensitive to carbon budget in the context of work trips. Results also show that PMCA scheme can lead to higher emissions for a path compared with the case without PMCA because of flow redistribution. The developed network equilibrium model allows to examine the change in system states at different carbon allocation levels and to design parameters of PMCA schemes accounting for population heterogeneity.     

Survivable millimeter-wave mesh networks

Millimeter-wave mesh networks have the potential to provide cost-effective high-bandwidth solutions to many current bandwidth-constrained networks including cellular backhaul. However, the availability of such networks is severely limited due to their susceptibility to weather, such as precipitation and humidity. In this paper, we present a rigorous approach to survivable millimeter-wave mesh networks based on experimentation, modeling, and simulation. Individual link performance is characterised using frame error-rate measurements from millimeter-wave transmissions on test links over a period of one year. A geometric model based on radar-reflectivity data is used to characterise rain storms and determine their impact on spatially correlated links of a mesh network. To mitigate the impact of link impairments on network services, we present two cross-layered routing protocols to route around the failures: P-WARP (predictive weather-assisted routing protocol) and XL-OSPF (cross-layered open shortest-path first). We conduct a performance analysis of the proposed mesh network under the presence of actual weather events as recorded by the US National Weather Service. Results show that the proposed approach yields the highest dependability when compared against existing routing methods.     

Volumetric Curved Planar Reformation for virtual endoscopy

Curved Planar Reformation (CPR) has proved to be a practical and widely used tool for the visualization of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for large, tubular, structures such as the trachea and the colon because abnormalities may be smaller relative to the size of the structure and may not have such distinct density and shape characteristics. Our new approach improves on this situation by using volume rendering for hollow regions and standard CPR for the surrounding tissue. This effectively combines gray scale contextual information with detailed color information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative to virtual endoscopy. Because the CPR and the volume rendering are tightly coupled, the projection method used has a significant effect on properties of the volume renderer, such as distortion and isometry. We describe and compare the different CPR projection methods and how they affect the volume rendering process. A version of the algorithm is also presented which makes use of importance driven techniques; this ensures the users attention is always focused on the area of interest and also improves the speed of the algorithm.     

Replica Placement Strategies in Data Grid

Replication is a technique used in Data Grid environments that helps to reduce access latency and network bandwidth utilization. Replication also increases data availability thereby enhancing system reliability. The research addresses the problem of replication in Data Grid environment by investigating a set of highly decentralized dynamic replica placement algorithms. Replica placement algorithms are based on heuristics that consider both network latency and user requests to select the best candidate sites to place replicas. Due to dynamic nature of Grid, the candidate site holds replicas currently may not be the best sites to fetch replicas in subsequent periods. Therefore, a replica maintenance algorithm is proposed to relocate replicas to different sites if the performance metric degrades significantly. The study of our replica placement algorithms is carried out using a model of the EU Data Grid Testbed 1 [Bell et al. Comput. Appl., 17(4), 2003] sites and their associated network geometry. We validate our replica placement algorithms with total file transfer times, the number of local file accesses, and the number of remote file accesses.     

Pressure measurements in laboratory-scale blast wave flow fields

The present study examines the effects that temporal and spatial averagings due to finite size and finite response time of pressure transducers have on the pressure measurements in blast wave flow fields generated by milligram charges of silver azide. In such applications, the characteristic time and length scales of the physical process are of the same order of magnitude as the temporal and spatial characteristics of the transducer. The measured pressure values will then be spatially and temporally averaged, and important parameters for the assessment of blast effects may not be properly represented in the measured trace. In this study, face-on and side-on pressure transducer setups are considered. In the experiments, face-on and side-on readings at the same distance from the charge as well as time-resolved optical visualization of the whole flow field are obtained simultaneously for the same explosive event. The procedure of data extraction from the experimental pressure traces is revisited and discussed in detail. In the numerical modeling part of the study, numerical blast flow fields are generated using an Euler flow solver. A numerical pressure transducer model is developed to qualitatively simulate the averaging effects. The experimental and numerical data show that the results of pressure measurements in experiments with small charges must be used with great caution. The effective averaging of the pressure signal may lead to a significant underestimation of blast wave intensities. The side-on setup is especially prone to this effect. The face-on setup provides results close to those obtained from optical records only if the pressure transducer is sufficiently remote from the charge.     

D.c. and a.c. electrical properties of vacuum evaporated thin SiO/GeO2 films

The d.c. and a.c. electrical properties were studied for various compositions of SiO/GeO₂ co-evaporated thin films carrying aluminium electrodes, in the temperature range 193–413 K. A.c. measurements were made over the frequency range 2x10²–10⁶Hz. The value of the d.c. activation energy was found to decrease with increasing GeO₂ content in the SiO. In the region of high applied field (above 10⁶ Vm^–1, the conduction mechanism is governed by Schottky emission at the blocking contact. The a.c. electrical conductivity, (), varies with frequency according to the relation () ^s, where the exponent s was found to be dependent on temperature and frequency. The a.c. conduction at low temperature was due to an electronic hopping process. The number of localized sites was estimated from the a.c. measurements for different compositions of SiO/GeO₂ using the models proposed by Elliott and by Pollak, and the values are compared. The Elliott model satisfactorily accounts for the observed a.c. electrical results. A correlation was found between activation energy, optical band gap, conductivity and number of localized sites for the various compositions of SiO/GeO₂ films. The relative dielectric constant, _r, and loss factor, tan , were found to increase with the increase of GeO₂ content in the films.