Abstract: Accurate software reliability prediction not only enables developers to improve the quality of software but also provides useful information to help them for planning valuable resources. This paper examines the performance of three well-known data mining techniques (CART, TreeNet and Random Forest) for predicting software reliability. We evaluate and compare the performance of proposed models with Cascade Correlation Neural Network (CCNN) using sixteen empirical databases from the Data and Analysis Center for Software. The goal of our study is to help project managers to concentrate their testing efforts to minimize the software failures in order to improve the reliability of the software systems. Two performance measures, Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Errors (MAE), illustrate that CART model is accurate than the models predicted using Random Forest, TreeNet and CCNN in all datasets used in our study. Finally, we conclude that such methods can help in reliability prediction using real-life failure datasets.
Abstract: In this paper, we analyze NEtwork MObility (NEMO) supporting problems in Content-Centric Networking (CCN), and propose the CCN-NEMO which can well support the deployment of the content-centric paradigm in large-scale mobile Internet. The CCN-NEMO extends the signaling message of the basic CCN protocol, to support the mobility discovery and fast trigger of Interest re-issuing during the network mobility. Besides, the Mobile Router (MR) is extended to optimize the content searching and relaying in the local subnet. These features can be employed by the nested NEMO to maximize the advantages of content retrieving with CCN. Based on the analysis, we compare the performance on handover latency between the basic CCN and our proposed CCN-NEMO. The results show that our scheme can facilitate the content-retrieving in the NEMO scenario with improved performance.
Abstract: Poly vinyl acetate (PVA)-based titania (TiO2)–carbon
nanotube composite nanofibers (PVA-TCCNs) with various
PVA-to-solvent ratios and PVA-based TiO2 composite nanofibers
(PVA-TN) were synthesized using an electrospinning process,
followed by thermal treatment. The photocatalytic activities of these
nanofibers in the degradation of airborne monocyclic aromatics under
visible-light irradiation were examined. This study focuses on the
application of these photocatalysts to the degradation of the target
compounds at sub-part-per-million indoor air concentrations. The
characteristics of the photocatalysts were examined using scanning
electron microscopy, X-ray diffraction, ultraviolet-visible
spectroscopy, and Fourier-transform infrared spectroscopy. For all the
target compounds, the PVA-TCCNs showed photocatalytic
degradation efficiencies superior to those of the reference PVA-TN.
Specifically, the average photocatalytic degradation efficiencies for
benzene, toluene, ethyl benzene, and o-xylene (BTEX) obtained using
the PVA-TCCNs with a PVA-to-solvent ratio of 0.3 (PVA-TCCN-0.3)
were 11%, 59%, 89%, and 92%, respectively, whereas those observed
using PVA-TNs were 5%, 9%, 28%, and 32%, respectively.
PVA-TCCN-0.3 displayed the highest photocatalytic degradation
efficiency for BTEX, suggesting the presence of an optimal
PVA-to-solvent ratio for the synthesis of PVA-TCCNs. The average
photocatalytic efficiencies for BTEX decreased from 11% to 4%, 59%
to 18%, 89% to 37%, and 92% to 53%, respectively, when the flow
rate was increased from 1.0 to 4.0 L min1. In addition, the average
photocatalytic efficiencies for BTEX increased 11% to ~0%, 59% to
3%, 89% to 7%, and 92% to 13%, respectively, when the input
concentration increased from 0.1 to 1.0 ppm. The prepared
PVA-TCCNs were effective for the purification of airborne aromatics
at indoor concentration levels, particularly when the operating
conditions were optimized.
Abstract: In this paper, we proposed a new framework to incorporate an intelligent agent software robot into a crisis communication portal (CCNet) in order to send alert news to subscribed users via email and other mobile services such as Short Message Service (SMS), Multimedia Messaging Service (MMS) and General Packet Radio Services (GPRS). The content on the mobile services can be delivered either through mobile phone or Personal Digital Assistance (PDA). This research has shown that with our proposed framework, the embodied conversation agents system can handle questions intelligently with our multilayer architecture. At the same time, the extended framework can take care of delivery content through a more humanoid interface on mobile devices.