Categorizing images in Web documents

The Web provides an increasingly powerful and popular publication mechanism. Web documents often contain a large number of images that serve various purposes. Identifying the functional categories of these images is an important task in Web repurposing. This article describes a study on the functional categorization of Web images using data collected from news Web sites. As the popularity of the Web soars, the content on the Web is increasingly accessed from wireless devices that have small screens and different bandwidths. Because many Web documents contain a large number of images serving different purposes, how to identify the function of each image so that it can be handled accordingly is an important issue in Web content repurposing. Much work remains to be done in function-based image classification of all images. Icons that appear regularly on Web sites (for example, newspaper logos) could be classified by analyzing different editions of the pages for repetitions. For the host class, a combination of detecting repetitive images and face recognition should help significantly.     

Empirical validation of object-oriented metrics for predicting fault proneness models

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.     

DSEF-1 is a member of the hnRNP H family of RNA-binding proteins and stimulates pre-mRNA cleavage and polyadenylation in vitro

DSEF-1 protein selectively binds to a G-rich auxiliary sequence element which influences the efficiency of processing of the SV40 late polyadenylation signal. We have obtained cDNA clones of DSEF-1 using sequence information from tryptic peptides isolated from DSEF-1 protein purified from HeLa cells. DSEF-1 protein contains three RNA-binding motifs and is a member of the hnRNP H family of RNA-binding proteins. Recombinant DSEF-1 protein stimulated the efficiency of cleavage and polyadenylation in an AAUAAA-dependent manner in in vitro reconstitution assays. DSEF-1 protein was shown to be able to interact with several poly(A) signals that lacked a G-rich binding site using a less stringent, low ionic strength gel band shift assay. Recombinant DSEF-1 protein specifically stimulated the processing of all of the poly(A) signals tested that contained a high affinity G-rich or low affinity binding site. DSEF-1 specifically increased the level of cross-linking of the 64 kDa protein of CstF to polyadenylation substrate RNAs. These observations suggest that DSEF-1 is an auxiliary factor that assists in the assembly of the general 3'-end processing factors onto the core elements of the polyadenylation signal.     

Establishment of Ad-Hoc Communities through Policy-Based Cryptography

A policy-based encryption scheme allows to encrypt a message according to a credential-based policy formalized as monotone Boolean expression written in standard normal form. The encryption is so that only the users having access to a qualified set of credentials for the policy are able to decrypt the message. In this paper, we first revisit the formal definition of policy-based encryption and describe a policy-based encryption scheme from bilinear pairings. Our scheme improves the one proposed in [W. Bagga and R. Molva. Policy-based cryptography and applications. In Proceedings of Financial Cryptography and Data Security (FC'05), volume 3570 of LNCS, pages 72–87. Springer-Verlag, 2005] in terms of ciphertext size, while at the same time preserving the computational efficiency. Then, we describe an application of policy-based encryption in the context of ad-hoc networks. More precisely, we show how the policy-based encryption primitive can be used to achieve a privacy-enhanced secure establishment of ad-hoc communities.     

A Survey of Various Schedulers Used for Fair Bandwidth Allocation in WiMAX: IEEE 802.16

ABSTRACT

Wireless Interoperability for Multiple Access (WiMAX) is one of the emerging fields of high-speed wireless communication that has enormous capabilities due to its range and the connection speed. Like wireless local area networks (LANs), WiMAX networks implement multiple quality of service (QoS) frameworks at the Media Access Control (MAC) level for assured data, voice, and video services. The question of ensuring QoS is basically how to distribute available resources to users in order to satisfy QoS parameters such as latency, jitter and throughput requirements. IEEE 802.16 standard does not have any particular guidelines on scheduling of incoming and outgoing data. This has caught the attention of researchers working on WiMAX. This article discusses the various issues in WiMAX along with a classification of various scheduling approaches based upon the type of scheduler for the sake of better understanding the scheduling problem and analyzing various available theories.     

Fabrication of coplanar microheater platform for LPG sensing applications

Microsystem Technologies - The design and fabrication of nichrome based coplanar microheater for LPG sensing applications is presented in this work. Nichrome based coplanar microheater is...     

Handling class imbalance problem in software maintainability prediction: an empirical investigation

As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP.     

Ultrasensitive detection of cancer biomarkers in the clinic by use of a nanostructured microfluidic array

Multiplexed biomarker protein detection holds unrealized promise for clinical cancer diagnostics due to lack of suitable measurement devices and lack of rigorously validated protein panels. Here we report an ultrasensitive electrochemical microfluidic array optimized to measure a four-protein panel of biomarker proteins, and we validate the protein panel for accurate oral cancer diagnostics. Unprecedented ultralow detection into the 5-50 fg·mL(-1) range was achieved for simultaneous measurement of proteins interleukin 6 (IL-6), IL-8, vascular endothelial growth factor (VEGF), and VEGF-C in diluted serum. The immunoarray achieves high sensitivity in 50 min assays by using off-line protein capture by magnetic beads carrying 400,000 enzyme labels and ~100,000 antibodies. After capture of the proteins and washing to inhibit nonspecific binding, the beads are magnetically separated and injected into the array for selective capture by antibodies on eight nanostructured sensors. Good correlations with enzyme-linked immunosorbent assays (ELISA) for protein determinations in conditioned cancer cell media confirmed the accuracy of this approach. Normalized means of the four protein levels in 78 oral cancer patient serum samples and 49 controls gave clinical sensitivity of 89% and specificity of 98% for oral cancer detection, demonstrating high diagnostic utility. The low-cost, easily fabricated immunoarray provides a rapid serum test for diagnosis and personalized therapy of oral cancer. The device is readily adaptable to clinical diagnostics of other cancers.