Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Asking the Right Questions To Solve Algebraic Word Problems(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2022) Çelik, Ege Yiğit; Orulluoğlu, Zeynel; Mertoğlu, Rıdvan; Tekir, SelmaWord algebra problems are among challenging AI tasks as they combine natural language understanding with a formal equation system. Traditional approaches to the problem work with equation templates and frame the task as a template selection and number assignment to the selected template. The recent deep learning-based solutions exploit contextual language models like BERT and encode the natural language text to decode the corresponding equation system. The proposed approach is similar to the template-based methods as it works with a template and fills in the number slots. Nevertheless, it has contextual understanding because it adopts a question generation and answering pipeline to create tuples of numbers, to finally perform the number assignment task by custom sets of rules. The inspiring idea is that by asking the right questions and answering them using a state-of-the-art language model-based system, one can learn the correct values for the number slots in an equation system. The empirical results show that the proposed approach outperforms the other methods significantly on the word algebra benchmark dataset alg514 and performs the second best on the AI2 corpus for arithmetic word problems. It also has superior performance on the challenging SVAMP dataset. Though it is a rule-based system, simple rule sets and relatively slight differences between rules for different templates indicate that it is highly probable to develop a system that can learn the patterns for the collection of all possible templates, and produce the correct equations for an example instance.Article Citation - WoS: 7Citation - Scopus: 12A Survey on Organizational Choices for Microservice-Based Software Architectures(TÜBİTAK, 2022) Ünlü, Hüseyin; Bilgin, Burak; Demirörs, OnurDuring the last decade, the demand for more flexible, responsive, and reliable software applications increased exponentially. The availability of internet infrastructure and new software technologies to respond to this demand led to a new generation of applications. As a result, cloud-based, distributed, independently deployable web applications working together in a microservice-based software architecture style have gained popularity. The style has been a common practice in the industry and successfully utilized by companies. Adopting this style demands software organizations to transform their culture. However, there is a lack of research studies that explores common practices for microservices. Thus, we performed a survey to explore the organizational choices on software analysis, design, size measurement, and effort estimation when working with microservices. The results provide a snapshot of the software industry that utilizes microservices. We provide insight for software organizations to transform their culture and suggest challenges researchers can focus on in the area.Article Citation - Scopus: 1Performance Analysis and Feature Selection for Network-Based Intrusion Detection With Deep Learning(Türkiye Klinikleri, 2022) Caner, Serhat; Erdoğmuş, Nesli; Erten, Yusuf MuratAn intrusion detection system is an automated monitoring tool that analyzes network traffic and detects malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion detection and classification performances of different deep learning based systems are examined. For this purpose, 24 deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore, the best performing model is utilized to inspect raw network traffic features and rank them with respect to their contributions to success rates. By selecting features with respect to their ranks, sets of varying size from 3 to 77 are assessed in terms of classification accuracy and time efficiency. The results show that recurrent neural networks with a certain level of complexity can achieve comparable success rates with state-of-the-art systems using a small feature set of size 9; while the average time required to classify a test sample is halved compared to the complete set.Article Citation - WoS: 1Citation - Scopus: 2Information Retrieval-Based Bug Localization Approach With Adaptive Attribute Weighting(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2021) ErşahIn, Mustafa; Utku, Semih; Kılınç, Deniz; ErşahIn, BuketSoftware quality assurance is one of the crucial factors for the success of software projects. Bug fixing has an essential role in software quality assurance, and bug localization (BL) is the first step of this process. BL is difficult and time-consuming since the developers should understand the flow, coding structure, and the logic of the program. Information retrieval-based bug localization (IRBL) uses the information of bug reports and source code to locate the section of code in which the bug occurs. It is difficult to apply other tools because of the diversity of software development languages, design patterns, and development standards. The aim of this study is to build an adaptive IRBL tool and make it usable by more companies. BugSTAiR solves the aforementioned problem by means of the adaptive attribute weighting (AAW) algorithm and is evaluated on four open-source projects which are well-known benchmark datasets on BL. One of them is BLIA which is the state of the art in bug localization area and another is BLUIR which is a well-known BL tool. According to the promising results of experiments, Top1 rank of BugSTAiR is 2% and MAP is 10% better than BLIA's results on AspectJ and it has localized 4.6% of all bugs in Top1 and its precision is 6.1% better than BLIA on SWT, respectively. On the other side, it is 20% better in the Top1 metric and 30% in precision than BLUIR.Article Citation - WoS: 2Citation - Scopus: 1Mutant Selection by Using Fourier Expansion(Türkiye Klinikleri Journal of Medical Sciences, 2020) Takan, Savaş; Ayav, TolgaMutation analysis is a widely used technique to evaluate the effectiveness of test cases in both hardware and software testing. The original model is mutated systematically under certain fault assumptions and test cases are checked against the mutants created to see whether the test cases can detect the faults or not. Mutation analysis is usually a computationally intensive task, particularly in finite state machine (FSM) testing due to a possibly huge amount of mutants. Random selection could be a practical reduction method under the assumption that each mutant is identical in terms of the probability of occurrence of its associating fault. The present study proposes a mutant selection method based on Fourier analysis of Boolean functions. Fourier helps to identify the most effective transitions on the output so that the mutants related to those transitions can be selected. Such mutants are considered more important since they are more likely to be killed. To evaluate the method, test cases are generated by the well-known W method, which has the capability of detecting every potential fault. The original and reduced sets of mutants are compared with respect to their importance values. Evaluations show that the mutants selected by the proposed technique are more effective, which reduces the cost of mutation analysis without sacrificing the performance of the mutation analysis.Article Estimating Spatiotemporal Focus of Documents Using Entropy With Pmi(Türkiye Klinikleri Journal of Medical Sciences, 2020) Yaşar, Damla; Tekir, SelmaMany text documents are spatiotemporal in nature, i.e. contents of a document can be mapped to a specific time period or location. For example, a news article about the French Revolution can be mapped to year 1789 as time and France as place. Identifying this time period and location associated with the document can be useful for various downstream applications such as document reasoning or spatiotemporal information retrieval. In this paper, temporal entropy with pointwise mutual information (PMI) is proposed to estimate the temporal focus of a document. PMI is used to measure the association of words with time expressions. Moreover, a word’s temporal entropy is considered as a weight to its association with a time point and a single time point with the highest overall score is chosen as the focus time of a document. The proposed method is generic in the sense that it can also be applied for spatial focus estimation of documents. In the case of spatial entropy with PMI, PMI is used to calculate the association between words and place entities. The effectiveness of our proposed methods for spatiotemporal focus estimation is evaluated on diverse datasets of text documents. The experimental evaluation confirms the superiority of our proposed temporal and spatial focus estimation methods.Article Citation - WoS: 2Privacy Issues in Post Dissemination on Facebook(Türkiye Klinikleri Journal of Medical Sciences, 2019) Sayın, Burcu; Şahin, Serap; Kogias, Dimitrios G.; Patrikakis, Charalampos Z.With social networks (SNs) being populated by a still increasing numbers of people who take advantage of the communication and collaboration capabilities that they offer, the probability of the exposure of people's personal moments to a wider than expected audience is also increasing. By studying the functionalities and characteristics that modern SNs offer, along with the people's habits and common behaviors in them, it is easy to understand that several privacy risks may exist, many of which people may be unaware of. In this paper, we focus on users' interactions with posts in a social network (SN), using Facebook as our research domain, and we emphasize some privacy leakages currently existing in Facebook's privacy policy. We also propose a solution to detected privacy issues, featuring a reference implementation of a tool based on a simulation, which visualizes the effect of potential privacy risks on Facebook and directs users to control their privacy. The proposed and simulated tool allows a post owner to observe the spreading area of his or her post depending on the selected privacy settings. Moreover, it provides preliminary feedback for all Facebook users that have interacted with this post, to make them aware of the possible privacy changes, aiming to give them a chance to protect the privacy of their interaction on this post by deleting it when an unwanted privacy change takes place. Finally, an online survey to increase privacy awareness in Facebook usage with over 500 volunteer participants has illuminated the need for such a tool or solution.Article Comparison of Group Key Establishment Protocols(Türkiye Klinikleri Journal of Medical Sciences, 2017) Şahin, Serap; Aslanoğlu, RabiaRecently group-oriented applications over unsecure open networks such as Internet or wireless networks have become very popular. Thus, group communication security over unsecure open networks has become a vital concern. Group key establishment (GKE) protocols are used to satisfy the confidentiality requirement of a newly started communication session by the generation or sharing of an ephemeral common key between the group members. In this study, we analyze the computation and communication efficiency of GKE protocols. Besides confidentiality, the security characteristics of identification and integrity control are also required for all steps of the protocol implementations. Thus, the main contribution of this work is to provide the computation and communication efficiency analysis of the same GKE protocols along with the identification of the group entities and integrity control of messages during the protocol steps. The specific implementation and analysis of GKE protocols are performed by group key agreement (GKA) with pairing- based cryptography and group key distribution (GKD) with verifiable secret sharing, respectively. Finally, a comparison of GKA and GKD protocols on the basis of their strong points and cost characteristics are also provided to inform potential users.Article Citation - WoS: 6Citation - Scopus: 1Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (university Level English Preparatory School Example)(Educational Consultancy and Research Center, 2014) Aslan, Burak Galip; Öztürk, Özlem; İnceoğlu, Mustafa MuratConsidering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles Questionnaire. The questionnaire was adapted to Turkish for this experimental study conducted with respect to the visual/verbal and active/reflective dimensions of the model. A topic in EFL was chosen for the learning content design, which was also carried into the digital domain and remastered as separate learning scenes for different learning styles. Computer software was also implemented to carry out the experimental learning processes. A quasi-experimental pre-test, post-test design was conducted with 46 volunteers, with 23 students assigned each to a control and an experimental group to compare academic achievement between student-based learning and conventional computer-based learning. No significant difference was found in academic achievement between the control and experimental groups after the experimental treatment. The diagnostic performance of the proposed student modeling system was also compared with performances from similar studies. This student modeling system had a successful prediction rate of 41% on the visual/verbal dimension and 54% on the active/reflective dimension, respectively.