RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine.     

Improving cache locking performance of modern embedded systems via the addition of a miss table at the L2 cache level

To confer the robustness and high quality of service, modern computing architectures running real-time applications should provide high system performance and high timing predictability. Cache memory is used to improve performance by bridging the speed gap between the main memory and CPU. However, the cache introduces timing unpredictability creating serious challenges for real-time applications. Herein, we introduce a miss table (MT) based cache locking scheme at level-2 (L2) cache to further improve the timing predictability and system performance/power ratio. The MT holds information of block addresses related to the application being processed which cause most cache misses if not locked. Information in MT is used for efficient selection of the blocks to be locked and victim blocks to be replaced. This MT based approach improves timing predictability by locking important blocks with the highest number of misses inside the cache for the entire execution time. In addition, this technique decreases the average delay per task and total power consumption by reducing cache misses and avoiding unnecessary data transfers. This MT based solution is effective for both uniprocessors and multicores. We evaluate the proposed MT-based cache locking scheme by simulating an 8-core processor with 2 levels of caches using MPEG4 decoding, H.264/AVC decoding, FFT, and MI workloads. Experimental results show that in addition to improving the predictability, a reduction of 21% in mean delay per task and a reduction of 18% in total power consumption are achieved for MPEG4 (and H.264/AVC) by using MT and locking 25% of the L2. The MT results in about 5% delay and power reductions on these video applications, possibly more on applications with worse cache behavior. For the FFT and MI (and other) applications whose code fits inside the level-1 instruction (I1) cache, the mean delay per task increases only by 3% and total power consumption increases by 2% due to the addition of the MT.     

Exploring the relationship between Internet ethics in university students and the big five model of personality

The widespread use of the Internet and the convenient mechanism it provides, such as easy access, easy downloads, and easy copy and paste functions have made many types of unethical behaviors easier, particularly those involving students in academic settings. Among the issues in ethics within the academic environment that can be triggered by the Internet are fraudulence, plagiarism, falsification, delinquency, unauthorized help, and facility misuse. Given these issues, the study seeks to investigate the extent to which students at a public university in Malaysia engage in such unethical behavior and their relationship with the big five personality model. This study was conducted using a survey method of 252 students in three different academic faculties. The results of factor analyses confirm and refine the reliability of the scales for both big five personality variables and unethical Internet behaviors as conceptualized through Internet triggered academic dishonesty (ITADS). The findings indicate that personality traits such as (1) agreeableness, (2) conscientiousness and (3) emotional stability are significantly and negatively correlated with unethical Internet behavior in university students. Significant differences in facility misuse are also observed between the three academic faculties investigated. This research should provide significant contributions to educators in designing the computer ethics curriculum and in allowing for educational institutions as well as other organizations in developing relevant policies and guidelines on matters pertaining to academic conduct, utilization of computers and Internet, and recruitment exercises.     

Forecasting conditional correlations in stock,bond and foreign exchange markets

The paper forecasts conditional correlations between three classes of international financial assets, namely stock, bond and foreign exchange. Two countries are considered, namely Australia and New Zealand. Forecasting will be conducted using three multivariate GARCH models, namely the CCC model [T. Bollerslev, Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model, Rev. Econ. Stat. 72 (1990) 498–505], VARMA-GARCH model [S. Ling, M. McAleer, Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory 19 (2003) 280–310], and VARMA-AGARCH model [M. McAleer, S. Hoti, F. Chan, Structure and asymptotic theory for multivariate asymmetric volatility, Econometric Rev., in press]. A rolling window technique is used to forecast 1-day ahead conditional correlations. To evaluate the impact of model specification on conditional correlations forecasts, this paper calculates and compares the correlations between conditional correlations forecasts resulted from the three models. The paper finds the evidence of volatility spillovers and asymmetric effect of negative and positive shock on the conditional variance in most pairs of series. However, it suggests that incorporating volatility spillovers and asymmetric do not contribute to better conditional correlations forecasts.     

Quantification of crustal geotherms along with their error bounds for seismically active regions of India: A Matlab toolbox

Determination of the thermo-mechanical structure of the crust for seismically active regions using available geophysical and geological data is of great importance. The most important feature of the intraplate earthquakes in the Indian region are that the seismicity occurs within the entire crust. In Latur region of India an earthquake occurred in the upper crust. In such situations, quantifying the uncertainties in seismogenic depths becomes very important. The stochastic heat conduction equation has been solved for different sets of boundary conditions, an exponentially decreasing radiogenic heat generation and randomness in thermal conductivity. Closed form analytical expressions for mean and variance in the temperature depth distribution have been used and an automatic formulation has been developed in Matlab for computing and plotting the thermal structure. The Matlab toolbox presented allows us to display the controlling thermal parameters on the screen directly, and plot the subsurface thermal structure along with its error bounds. The software can be used to quantify the thermal structure for any given region and is applied here to the Latur earthquake region of India.     

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.     

Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays

This paper is concerned with the problem of asymptotic stability of neutral type Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional (LKF), reciprocal convex technique and Jensen’s inequality are used to delay-dependent conditions are established to analysis the asymptotic stability of Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. These stability conditions are formulated as linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. Finally numerical examples are given to illustrate the usefulness of our proposed method.     

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.