Multi‐level reranking approach for bug localization

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi‐level re‐ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi‐level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets.     

An integrated approach to concept evaluation in a new product development

A new product development (NPD) process can be thought as a comprehensive process in which the design is progressively detailed through a series of phases. At the end of each phase a design review is held to approve the design and release or not it to the next level. As one of these phases, concept selection aiming to select the most appropriate concept for further development, is conducted earlier in the process. As the further development progresses on a selected concept, it becomes more difficult to make design changes in terms of cost and schedule dimensions, and therefore, selecting the best concept among a set of available alternatives has been an important issue for companies. On the other hand, in the presence of many alternatives and selection criteria, the selection problem becomes a multiple-criteria decision making concept selection problem. To solve this problem, in this work, an integrated approach bringing two popular methods together: the modified technique for order preference by similarity to ideal solution (TOPSIS) and the analytical network process (ANP). The ANP method is used to determine the relative weights of a set of quantitative and qualitative evaluation criteria, as the modified TOPSIS method utilized to rank competing concept alternatives. In addition, a real example is presented to demonstrate the effectiveness and applicability of the proposed approach for potential practitioners and readers.     

Inner approximations of completely positive reformulations of mixed binary quadratic programs: a unified analysis

Every quadratic programming problem with a mix of continuous and binary variables can be equivalently reformulated as a completely positive optimization problem, that is, a linear optimization problem over the convex but computationally intractable cone of completely positive matrices. In this paper, we focus on general inner approximations of the cone of completely positive matrices on instances of completely positive optimization problems that arise from the reformulation of mixed binary quadratic programming problems. We provide a characterization of the feasibility of such an inner approximation as well as the optimal value of a feasible inner approximation. In particular, our results imply that polyhedral inner approximations are equivalent to a finite discretization of the feasible region of the original completely positive optimization problem. Our characterization yields, as a byproduct, an upper bound on the gap between the optimal value of an inner approximation and that of the original instance. We discuss the implications of this error bound for standard and box-constrained quadratic programs as well as general mixed binary quadratic programs with a bounded feasible region.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Evaluation of a university website’s usability for visually impaired students

Today websites are the tools most commonly used to access information. People with disabilities face difficulties accessing or using information, and the importance of website usability in their lives needs to be recognized. Visually impaired students need to be able to use university websites that inform them about the opportunities and events taking place on campus. This study aims to evaluate the usability of a university website by visually impaired students. In this research, six visually impaired students were interviewed. The assistive technologies they use, as well as the various web pages they wished to use unaided were identified. Following data collection, usability tests were conducted and satisfaction surveys were completed. The usability test was done with five visually impaired students. They were asked to think aloud while performing 11 tasks involving their university’s web pages, including the main page and the pages of student affairs, library and departments and then to accomplish these tasks. In this test, five tasks were not successfully completed by all students. According to the test results, finding final exam dates on the academic calendar posed major difficulties, and accessing the course schedule web page was the task that required the most time. The test results indicated the need for a search engine on each page, a text version for all pages, rearrangement of the web link sequences with tabs and more information about visuals. Suggestions related to the visually impaired students’ needs were offered.     

Decoupled approach to integrated optimum design of structures and robust control systems

Integrated optimum design of structures and control systems is studied by using H ₂ and robust control formulations. It is derived that conventional simultaneous optimization approach by using these robust control laws can be approximated by a decoupled optimization approach in which the structures are optimized by shaping the structural singular values and then the controllers can be designed, namely, decoupled, sequential or successive design approach. It is shown that the proposed decoupled optimization approach can be used to design optimum robust structures and has certain advantages over the conventional simultaneous optimization procedures such as it avoids the drawbacks of pure robust control laws and faster, especially if the number of degrees of freedom (DOF) of the associated structure is large. The bounds for achievable robustness measures are also obtained. Following, simultaneous and decoupled optimization approaches are applied to active control of two structures. The optimization results are presented, and it is concluded that the proposed decoupled optimization approach yields the achieved global minimum much faster than the simultaneous optimization approach.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Quantum chemical studies on protonation of some substituted thiazole derivatives

The acidity constants, pK(a) values for protonation of some substituted thiazole derivatives were calculated by using AM1 and PM3 basis sets of semi-empirical methods and B3LYP/6-31G(d) basis sets of density functional theory (DFT) calculated physical and thermodynamic parameters. Correlation search among the experimental and calculated acidity constants, pK(a) values, revealed that the best correlation exist between the experimental and ab initio calculated pK(a) values with a regression of R(2)=0.98.     

Efficient storage of healthcare data in XML-based smart cards

In this paper we present storage structures for efficiently processing XML path queries on healthcare data stored in smart cards using XML format. In the design of the storage structures that we present, properties of healthcare data and fundamental types of queries on the healthcare data are taken into account as well as the constraints of the smart cards. We compare the space and time efficiency of the proposed storage structures for various types of queries.     

Use of sesame stalk biomass for the removal of Ni(II) and Zn(II) from aqueous solutions

In this study an agricultural residue, sesame stalk, was evaluated for the removal of Ni(II) and Zn(II) metal ions from aqueous solutions. Biosorption studies were carried out at different pH, biosorbent dosage, initial metal ion concentrations, contact time, and solution temperature to determine the optimum conditions. The experimental data were modeled by Langmuir, Freundlich, Dubinin-Radushkevich (D-R) and Temkin isotherm models. Langmuir model resulted in the best fit of the biosorption data. The pseudo-first-order and pseudo-second-order kinetic models were used to describe the kinetic data and to evaluate rate constants. The best correlation was provided by the second-order kinetic model. The thermodynamic parameters such as ΔG°, ΔH° and ΔS° were calculated for predicting the nature of adsorption. The experimental results showed that sesame stalk can be used as an effective and low-cost biosorbent precursor for the removal of heavy metal ions from aqueous solutions.