Computer Engineering / Bilgisayar Mühendisliği

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  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    The Relation Between Bug Fix Change Patterns and Change Impact Analysis
    (Institute of Electrical and Electronics Engineers, 2021) Ufuktepe,E.; Tuglular,T.; Palaniappan,K.
    Change impact analysis analyzes the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. In this study, we analyze the bug fix change patterns to have a better understanding of what types of changes are common in fixing bugs. To achieve this, we implemented a tool that compares two versions of codes and detects the changes that are made. Then, we investigated how these changes are related to change impact analysis. In our case study, we used 13 of the projects and 621 bugs from Defects4J to identify the common change types in bug fixed. Then, to find the change types related to cause an impact in the software, we performed an impact analysis on a subset of projects and bugs of Defects4J. The results have shown that, on average, 90% of the bug fix change types are adding a new method declaration and changing the method body. Then, we investigated if these changes cause an impact or a ripple effect in the software by performing a Markov chain-based change impact analysis. The results show that the bug fix changes had only impact rates within a range of 0.4%-5%. Furthermore, we performed a statistical correlation analysis to find if any of the bug fixes have a significant correlation on the impact of change. The results have shown that there is a negative correlation between caused impact with the change types adding new method declaration and changing method body. On the other hand, we found that there is a positive correlation between caused impact and changing the field type. © 2021 IEEE.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Application of Human-Robot Interaction Features To Design and Purchase Processes of Home Robots
    (Springer, 2021) Yapıcı, Nur Beril; Tuğlular, Tuğkan; Başoğlu, Ahmet Nuri
    Production of home robots, such as robotic vacuum cleaners, currently focuses more on the technology and its engineering than the needs of people and their interaction with robots. An observation supporting this view is that the home robots are not customizable. In other words, buyers cannot select the features and built their home robots to order. Stemmed from this observation, the paper proposes an approach that starts with a classification of features of home robots. This classification concerns robot interaction with humans and the environment, a home in our case. Following the classification, the proposed approach utilizes a new hybrid model based on a built-to-order model and dynamic eco-strategy explorer model, enabling designers to develop a production line and buyers to customize their home robots with the classified features. Finally, we applied the proposed approach to robotic vacuum cleaners. We developed a feature model for robotic vacuum cleaners, from which we formed a common uses scenario model.
  • Article
    Performance and Accuracy Predictions of Approximation Methods for Shortest-Path Algorithms on Gpus
    (Elsevier, 2022) Aktılav, Busenur; Öz, Işıl
    Approximate computing techniques, where less-than-perfect solutions are acceptable, present performance-accuracy trade-offs by performing inexact computations. Moreover, heterogeneous architectures, a combination of miscellaneous compute units, offer high performance as well as energy efficiency. Graph algorithms utilize the parallel computation units of heterogeneous GPU architectures as well as performance improvements offered by approximation methods. Since different approximations yield different speedup and accuracy loss for the target execution, it becomes impractical to test all methods with various parameters. In this work, we perform approximate computations for the three shortest-path graph algorithms and propose a machine learning framework to predict the impact of the approximations on program performance and output accuracy. We evaluate random predictions for both synthetic and real road-network graphs, and predictions of the large graph cases from small graph instances. We achieve less than 5% prediction error rates for speedup and inaccuracy values.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content
    (World Scientific Publishing, 2021) Sezerer, Erhan; Tenekeci, Samet; Acar, Ali; Baloğlu, Bora; Tekir, Selma
    In the field of software engineering, practitioners' share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors' mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves 63.8% accuracy in binary classification in SO design patterns tag and 71.6% accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system's predicted reputation labels match the quality of provided answers.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 7
    Human-Robot Interfaces of the Neuroboscope: a Minimally Invasive Endoscopic Pituitary Tumor Surgery Robotic Assistance System
    (ASME, 2021) Dede, Mehmet İsmet Can; Kiper, Gökhan; Ayav, Tolga; Özdemirel, Barbaros; Tatlıcıoğlu, Enver; Hanalioğlu, Şahin; Işıkay, İlkay
    Endoscopic endonasal surgery is a commonly practiced minimally invasive neurosurgical operation for the treatment of a wide range of skull base pathologies including pituitary tumors. A common shortcoming of this surgery is the necessity of a third hand when the endoscope has to be handled to allow active use of both hands of the main surgeon. The robot surgery assistant NeuRoboScope system has been developed to take over the endoscope from the main surgeon's hand while providing the surgeon with the necessary means of controlling the location and direction of the endoscope. One of the main novelties of the NeuRoboScope system is its human-robot interface designs which regulate and facilitate the interaction between the surgeon and the robot assistant. The human-robot interaction design of the NeuRoboScope system is investigated in two domains: direct physical interaction (DPI) and master-slave teleoperation (MST). The user study indicating the learning curve and ease of use of the MST is given and this paper is concluded via providing the reader with an outlook of possible new human-robot interfaces for the robot assisted surgery systems.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Catadioptric Hyperspectral Imaging, an Unmixing Approach
    (Institution of Engineering and Technology, 2020) Özışık Başkurt, Didem; Baştanlar, Yalın; Yardımcı Çetin, Yasemin
    Hyperspectral imaging systems provide dense spectral information on the scene under investigation by collecting data from a high number of contiguous bands of the electromagnetic spectrum. The low spatial resolutions of these sensors frequently give rise to the mixing problem in remote sensing applications. Several unmixing approaches are developed in order to handle the challenging mixing problem on perspective images. On the other hand, omnidirectional imaging systems provide a 360-degree field of view in a single image at the expense of lower spatial resolution. In this study, we propose a novel imaging system which integrates hyperspectral cameras with mirrors so on to yield catadioptric omnidirectional imaging systems to benefit from the advantages of both modes. Catadioptric images, incorporating a camera with a reflecting device, introduce radial warping depending on the structure of the mirror used in the system. This warping causes a non-uniformity in the spatial resolution which further complicates the unmixing problem. In this context, a novel spatial-contextual unmixing algorithm specifically for the large field of view of the hyperspectral imaging system is developed. The proposed algorithm is evaluated on various real-world and simulated cases. The experimental results show that the proposed approach outperforms compared methods.
  • Conference Object
    On-board applications development via symbolic user interfaces
    (Springer, 2014) Kumova, Bora İsmail
    becerik is a functional language consisting of symbolic commands for managing and composing applications. Application commands consist of symbols that are associated with reading sensor values, computing those values and executing actuator values. It is the result of a co-design of mechatronic functionality and robotic behaviour. The requirements given for mechatronic functionality were those of simple robotics kits that are used in school education or as toys. The requirements given for the behaviour were to provide a reflexive one, consisting of triggering simple computations and actuations from simple sensor values. becerik currently lives as a leJOS application on NXT robots and enables developing simple applications using the standard display and buttons of the NXT brick. In this paper we introduce the symbolic user interfaces of becerik. © 2014 Springer International Publishing Switzerland.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 14
    Rule-Based Automatic Question Generation Using Semantic Role Labeling
    (Institute of Electronics, Information and Communication Engineers, 2019) Keklik, Onur; Tuğlular, Tuğkan; Tekir, Selma
    This paper proposes a new rule-based approach to automatic question generation. The proposed approach focuses on analysis of both syntactic and semantic structure of a sentence. Although the primary objective of the designed system is question generation from sentences, automatic evaluation results shows that, it also achieves great performance on reading comprehension datasets, which focus on question generation from paragraphs. Especially, with respect to METEOR metric, the designed system significantly outperforms all other systems in automatic evaluation. As for human evaluation, the designed system exhibits similar performance by generating the most natural (human-like) questions.
  • Article
    Citation - Scopus: 1
    Curve Description by Histograms of Tangent Directions
    (Institution of Engineering and Technology, 2019) Köksal, Ali; Özuysal, Mustafa
    The authors propose a novel approach for the description of objects based on contours in their images using real-valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture-free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture-based descriptors such as scale-invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.
  • Conference Object
    Citation - Scopus: 1
    Fuzzy-Syllogistic Systems: a Generic Model for Approximate Reasoning
    (Springer, 2016) Kumova, Bora İsmail
    The well known Aristotelian syllogistic system S consists of 256 moods. We have found earlier that 136 moods are distinct in terms of equal truth ratios that range in tau = [ 0,1]. The truth ratio of a particular mood is calculated by relating the number of true and false syllogistic cases that the mood matches. The introduction of (n -1) fuzzy existential quantifiers, extends the system to fuzzy-syllogistic systems S-n, 1 < n, of which every fuzzy-syllogistic mood can be interpreted as a vague inference with a generic truth ratio, which is determined by its syllogistic structure. Here we introduce two new concepts, the relative truth ratio (r)tau = [ 0,1] that is calculated from the cardinalities of the syllogistic cases of the mood and fuzzy-syllogistic ontology (FSO). We experimentally apply the fuzzy-syllogistic systems S-2 and S-6 as underlying logic of a FSO reasoner (FSR) and discuss sample cases for approximate reasoning.y