Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

Palmprint Recognition Based on Complete Direction Representation

Abstract: Direction information serves as one of the most important features for palmprint recognition In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale Hence, they did not fully utilize all potentials of DR In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain In this paper, we propose a general framework for DR-based method named complete DR (CDR), which reveals DR by a comprehensive and complete way Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning This way, CDR subsumes previous methods as special cases Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance Motived this way, we propose a novel palmprint recognition algorithm in frequency domain First, we extract CDR using multi-scale modified finite radon transformation Then, an effective correlation filter, namely, band-limited phase-only correlation, is explored for pattern matching To remove feature redundancy, the sequential forward selection method is used to select a small number of CDR images Finally, the matching scores obtained from different selected features are integrated using score-level-fusion Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications

Architecture of Cobweb-Based Redundant TSV for Clustered Faults

Abstract: In this brief, a cobweb-based redundant through-silicon-via (TSV) design is proposed with efficient hardware as well as high repair rate to repair clustered faulty TSVs (FTSVs). The experimental simulation results demonstrate that for highly clustered faults, the repair rate of the proposed RTSV method is 48.59% and 1.75% higher than that of the ring-based and router-based RTSV methods, respectively. Furthermore, the proposed design can achieve 63.93% and 16.34% hardware reductions compared with the router-based and the ring-based design, respectively.

Towards Effective Bug Triage with Software Data Reduction Techniques

Abstract: Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.

Product Aspect Ranking and Its Applications

Abstract: Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both firms and users. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions on the product. In particular, given the consumer reviews of a product, we first identify product aspects by a shallow dependency parser and determine consumer opinions on these aspects via a sentiment classifier. We then develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions. The experimental results on a review corpus of 21 popular products in eight domains demonstrate the effectiveness of the proposed approach. Moreover, we apply product aspect ranking to two real-world applications, i.e., document-level sentiment classification and extractive review summarization, and achieve significant performance improvements, which demonstrate the capacity of product aspect ranking in facilitating real-world applications.

Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems

Abstract: The collection and combination of assessment data in trustworthiness evaluation of cloud service is challenging, notably because QoS value may be missing in offline evaluation situation due to the time-consuming and costly cloud service invocation. Considering the fact that many trustworthiness evaluation problems require not only objective measurement but also subjective perception, this paper designs a novel framework named CSTrust for conducting cloud service trustworthiness evaluation by combining QoS prediction and customer satisfaction estimation. The proposed framework considers how to improve the accuracy of QoS value prediction on quantitative trustworthy attributes, as well as how to estimate the customer satisfaction of target cloud service by taking advantages of the perception ratings on qualitative attributes. The proposed methods are validated through simulations, demonstrating that CSTrust can effectively predict assessment data and release evaluation results of trustworthiness.