Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Article Effect of Belief in Free Will on the Intensity of Third-Party Punishment(Istanbul University Press, 2025) Çakar, Tuna; Erözden, Ozan; Akyürek, Güçlü; Özen, Zeynep; Şahin, Türkay; Keskin, İrem Nur; Ünlü, Meryem; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 05. Faculty of Law; 01. MEF UniversityThe institutionalized criminal justice mechanisms are built on two psychological and social traits: third-party punishment (TPP) and belief in free will (BFW). TPP is the administration of a sanction to a transgressor by an individual not affected by the transgression. BFW posits that humans are in control of their actions. Previous studies have indicated that BFW influences TPP. The aim of this study is to investigate whether the level of BFW has an impact on the magnitude of punishment in TPP tasks. Furthermore, it questions whether the degree of affective arousal of the punisher creates an additional effect on the magnitude of the punishment. Our basic hypothesis is that the BFW and punishment magnitude are positively correlated. We also hypothesize that the expected positive correlation between BFW and punishment magnitude will be more manifest in low-affect scenarios than in high-affect ones. Participants (N = 726) were given 49 hypothetical crime scenarios categorized as low- and high-affect cases. Upon reading each scenario, the participants were tasked to attribute a penalty between the two given options. Our results showed that the level of BFW was positively correlated with the degree of punishment administered in the hypothetical crime scenarios and that the average punishment magnitude for participants with a low level of BFW increased in the high-affect crime scenarios. We assume that our results would shed light on the underlying causes of public reactions to criminal sentencing policies, thus helping lawmakers in enacting better regulations in this respect. 2025. Çakar, T., Akyürek, G., Erözden, O., Şahin, T., Keskin, İ. N., Ünlü, M., Özen, D. H. & Özen, Z.Master Thesis EAFT: Evolutionary algorithms for GCC flag tuning(MEF Üniversitesi, 2023) Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe runtime of written codes is a matter of great importance, especially for code that is compiled once and executed multiple times. It is very important for developers to ensure that the resources required by a code are used as efficiently as possible, and that the runtime is as low as possible. Developers who use compilers such as GCC or LLVM to compile and run code written in C or C++ can optimize their code manually and, with certain optimization pointers, are able to make it run faster. This will provide the shorter runtime, but completıng this manual optimization is within the abilities of every developer since determining the right combination from more than 200 flags requires significant expertise. Many studies have tackled this issue. In this study, Evolutionary Algorithms for GCC Flag Tuning (EAFT) have been developed as a solution to this problem. This Autotuner, which is completely open-source, runs the code provided by the end user according to the specifications also selected by the end user, and searches for the most suitable optimization markers. For the code to be given In line with this study, which specifically addresses the end user, the user can input the code path directly from the Terminal, as well as specify the selection method and the crossover to be used. These choices can be made without the need to alter the code. The genetic algorithm and particle swarm optimization to be used is also presented to the user in EAFT, and unlike in other studies, genetic algorithm contain not one but several models.Conference Object Citation - Scopus: 1Does Prompt Engineering Help Turkish Named Entity Recognition?(Institute of Electrical and Electronics Engineers Inc., 2024) Pektezol, A.S.; Ulugergerli, A.B.; Demir, Şeniz; Demir, Şeniz; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe extraction of entity mentions in a text (named entity recognition) has been traditionally formulated as a sequence labeling problem. In recent years, this approach has evolved from recognizing entities to answering formulated questions related to entity types. The questions, constructed as prompts, are used to elicit desired entity mentions and their types from large language models. In this work, we investigated prompt engineering in Turkish named entity recognition and studied two prompting strategies to guide pretrained language models toward correctly identifying mentions. In particular, we examined the impact of zero-shot and few-shot prompting on the recognition of Turkish named entities by conducting experiments on two large language models. Our evaluations using different prompt templates revealed promising results and demonstrated that carefully constructed prompts can achieve high accuracy on entity recognition, even in languages with complex morphology. © 2024 IEEE.Conference Object Citation - Scopus: 1Artificial Intelligence Driven Multivariate Time Series Analysis of Network Traffic Prediction(Institute of Electrical and Electronics Engineers Inc., 2024) Filiz, G.; Yıldız, A.; Çakar, Tuna; Altıntaş, S.; Çakar, T.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R2). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service. © 2024 IEEE.Article Citation - Scopus: 5Investigation of the Motion of a Spherical Object Located at Soft Elastic and Viscoelastic Material Interface for Identification of Material Properties(Academic Enhancement Department, King Mongkut's University of Technology North Bangkok, 2023) Körük, Hasan; Körük, Hasan; 02.03. Department of Mechanical Engineering; 02. Faculty of Engineering; 01. MEF UniversityMeasuring the properties of soft viscoelastic materials is challenging. Here, the motion of a spherical object located at the soft elastic and viscoelastic material interface for the identification of material properties is thoroughly investigated. Formulations for different loading cases were derived. First, the theoretical models for a spherical object located at an elastic medium interface were derived, ignoring the medium viscosity. After summarizing the model for the force reducing to zero following the initial loading, we developed mathematical models for the force reducing to a lower non-zero value or increasing to a higher non-zero value, following the initial loading. Second, a similar derivation process was followed to evaluate the response of a spherical object located at a viscoelastic medium interface. Third, by performing systematic analyses, the theoretical models obtained via different approaches were compared and evaluated. Fourth, the measured and predicted responses of a spherical object located at a gelatin phantom interface were compared and the viscoelastic material properties were identified. It was seen that the frequency of oscillations of a spherical object located at the sample interface during loading was 10–15% different from that during unloading in the experimental studies here. The results showed that different loading cases have immense practical value and the formulations for different loading cases can provide an accurate determination of material properties in a multitude of biomedical and industrial applications. © 2023 King Mongkut’s University of Technology North Bangkok. All Rights Reserved.Article Citation - WoS: 15Citation - Scopus: 15Enhanced Primordial Gravitational Waves From a Stiff Postinflationary Era Due To an Oscillating Inflaton(Amer Physical Soc, 2024) Chen, Chao; Dimopoulos, Konstantinos; Eroncel, Cem; Ghoshal, Anish; 01. MEF UniversityWe investigate two classes of inflationary models, which lead to a stiff period after inflation that boosts the signal of primordial gravitational waves (GWs). In both families of models studied, we consider an oscillating scalar condensate, which when far away from the minimum is overdamped by a warped kinetic term, a la alpha-attractors. This leads to successful inflation. The oscillating condensate is in danger of becoming fragmented by resonant effects when nonlinearities take over. Consequently, the stiff phase cannot be prolonged enough to enhance primordial GWs at frequencies observable in the near future for low orders of the envisaged scalar potential. However, this is not the case for a higher-order scalar potential. Indeed, we show that this case results in a boosted GW spectrum that overlaps with future observations without generating too much GW radiation to destabilize big bang nucleosynthesis. For example, taking alpha=O(1), we find that the GW signal can be safely enhanced up to Omega(GW) (f)similar to 10(-11) at frequency f similar to 10(2) Hz, which will be observable by the Einstein Telescope. Our mechanism ends up with a characteristic GW spectrum, which if observed, can lead to the determination of the inflation energy scale, the reheating temperature, and the shape (steepness) of the scalar potential around the minimum.Article Mention Detection in Turkish Coreference Resolution(Tubitak Scientific & Technological Research Council Turkey, 2024) Demir, Seniz; Akdag, Hanifi Ibrahim; Demir, Şeniz; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityA crucial step in understanding natural language is detecting mentions that refer to real-world entities in a text and correctly identifying their boundaries. Mention detection is commonly considered a preprocessing step in coreference resolution which is shown to be helpful in several language processing applications such as machine translation and text summarization. Despite recent efforts on Turkish coreference resolution, no standalone neural solution to mention detection has been proposed yet. In this article, we present two models designed for detecting Turkish mentions by using feed-forward neural networks. Both models extract all spans up to a fixed length from input text as candidates and classify them as mentions or not mentions. The models differ in terms of how candidate text spans are represented. The first model represents a span by focusing on its first and last words, whereas the representation also covers the preceding and proceeding words of a span in the second model. Mention span representations are formed by using contextual embeddings, part-of-speech embeddings, and named-entity embeddings of words in interest where contextual embeddings are obtained from pretrained Turkish language models. In our evaluation studies, we not only assess the impact of mention representation strategies on system performance but also demonstrate the usability of different pretrained language models in resolution task. We argue that our work provides useful insights to the existing literature and the first step in understanding the effectiveness of neural architectures in Turkish mention detection.Conference Object Citation - Scopus: 1The Application of Two Bayesian Personalized Ranking Approaches Based on Item Recommendation From Implicit Feedback(Ieee, 2024) Tagtekin, Burak; Sahin, Zeynep; Çakar, Tuna; Drias, Yassine; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.Article Citation - WoS: 6Citation - Scopus: 8Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies(Elsevier, 2024) Karaca, Elif; Çakar, Tuna; Karaca, Mehmet; Gul, Hasan Huseyin Mirac; Hüseyin Miraç Gül, Hasan; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study explores the alignment of specific landscape features within school environments with the core elements of Attention Restoration Theory (ART) that includes Coherence, Fascination, Compatibility, and Being Away. Utilizing Kansei Engineering, this research integrates emotional analysis into landscape design by employing Virtual Reality (VR) and Electroencephalogram (EEG) technologies to record students' responses to different landscape simulations. Analytical techniques, including the Taguchi Method and Analysis of Variance (ANOVA), were applied to evaluate the data. The findings have revealed that students associate a sense of enclosure with a coherent landscape and openness with a fascinating landscape, the lawn's significance was also highlighted for coherent landscape. However, limited insights were gained regarding Compatibility and Being Away. The study advocates for diverse cognitive zones within school landscapes to promote mental restoration, emphasizing the need for varied design elements that cater to the elevated experience of students.Conference Object Analyzing Customer Churn: a Comparative Study of Machine Learning Models on Pay-Tv Subscribers in Turkey(IEEE, 2023) Obalı, Emir; Çakar, Tuna; Karani Yılmaz, Veysel; Kara, Erkan; Meşe, Yasemin Kürtcü; Çakar, Tuna; Yıldız, Ayşenur; Hataş, Tuğce Aydın; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityUnderstanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.