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Yayın Implementing lightweight, dynamic hierarchical key assignment scheme for cloud computing(IEEE, 2024-03-25) Çelikbilek, İbrahim; Çeliktaş, Barış; Özdemir, EnverIn this paper, we propose the implementation and adaptation of a hierarchical key assignment scheme (HKAS) previously developed in our research to improve access control in cloud computing environments. The secret keys generated and managed by this scheme can be utilized for various purposes within the cloud computing, including data encryption, integrity checks, secure communications, and accessing critical infrastructures or services. Our implementation performs dynamic update operations with minimal computational cost and storage demands, as users within the hierarchical structure do not store any key components. Through security analysis, the scheme demonstrates strong key indistinguishability security (S-KI-security), effectively safeguarding keys against various cryptographic attacks. The scheme's flexibility allows it to be tailored to specific organizational needs, whether for securing sensitive data, ensuring compliance with regulatory standards, or facilitating secure data sharing and collaboration in cloud environments. Thus, we advocate for the practical implementation of the HKAS in transitioning to cloud environments.Yayın Leveraging transformer-based language models for enhanced service insight in tourism(IEEE, 2023-12-22) Er, Aleyna; Özçelik, Şuayb TalhaCustomer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning.Yayın Forecasting and analysis of energy consumption and waste generation in Antalya with SVR(IEEE, 2023-12-24) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalAntalya, a rapidly expanding coastal city in Türkiye, has experienced significant changes due to urbanization and increasing tourism activities. Comprehending tourism trends is crucial for the city's sustainable development and environmental management. Based on this perspective, this paper aims to present a comprehensive retrospective analysis of Antalya's energy consumption, domestic solid waste generation, wastewater generation, population growth, and tourist numbers over the years. Antalya faces significant challenges due to escalating trends in listed areas. Utilizing the Support Vector Regression, this study projects a need for an additional 1715 GWh of electricity production capacity, an expansion of wastewater capacity by 85639 thousand m3, and an increase in domestic solid waste disposal capacity by 597745 tons by 2028 to accommodate growing demands. We emphasize the importance of adopting effective policies and strategies to support energy efficiency, waste reduction, and wastewater management alongside sustainable urban planning and tourism management for Antalya's long-Term environmental sustainability and development. The findings presented in this study provide valuable insights for policymakers, urban planners, and stakeholders to make informed decisions, ensuring a balanced approach toward economic growth and environmental conservation.Yayın Application of ChatGPT in the tourism domain: potential structures and challenges(IEEE, 2023-12-23) Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanThe tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.Yayın AnlamVer: Semantic model evaluation dataset for Turkish - word similarity and relatedness(Association for Computational Linguistics (ACL), 2018-08-26) Ercan, Gökhan; Yıldız, Olcay TanerIn this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.Yayın TUR2SQL: A cross-domain Turkish dataset for Text-to-SQL(IEEE, 2023-09-15) Kanburoğlu, Ali Buğra; Tek, Faik BorayThe field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years. While existing Text-to-SQL datasets primarily focus on English and other languages such as Chinese, there is a lack of resources for the Turkish language. In this study, we introduce the first publicly available cross-domain Turkish Text-to-SQL dataset, named TUR2SQL. This dataset consists of 10,809 pairs of natural language statements and their corresponding SQL queries. We conducted experiments using SQLNet and ChatGPT on the TUR2SQL dataset. The experimental results show that SQLNet has limited performance and ChatGPT has superior performance on the dataset. We believe that TUR2SQL provides a foundation for further exploration and advancements in Turkish language-based Text-to-SQL research.Yayın Hotel sales forecasting with LSTM and N-BEATS(IEEE, 2023-09-15) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalTime series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming (heat, water level), and hotel sales forecasting. This paper focuses on hotel sales forecasting with machine learning and deep learning solutions. A simple forecast solution is to repeat the last observation (Naive method) or the average of the past observations (Average method). More sophisticated solutions have been developed over the years, such as machine learning methods that have linear (Linear Regression, ARIMA) and nonlinear (Polynomial Regression and Support Vector Regression) methods. Different kinds of neural networks are developed and used in time series forecasting problems, and two of the successful ones are Recurrent Neural Networks and N-BEATS. This paper presents a forecasting analysis of hotel sales from Türkiye and Cyprus. We showed that N-BEATS is a solid choice against LSTM, especially in long sequences. Moreover, N-BEATS has slightly better inference time results in long sequences, but LSTM is faster in short sequences.Yayın ISIKSumm at BioLaySumm task 1: BART-based summarization system enhanced with Bio-entity labels(Association for Computational Linguistics (ACL), 2023-07-13) Çolak, Çağla; Karadeniz, İlknurCommunicating scientific research to the general public is an essential yet challenging task. Lay summaries, which provide a simplified version of research findings, can bridge the gap between scientific knowledge and public understanding. The BioLaySumm task (Goldsack et al., 2023) is a shared task that seeks to automate this process by generating lay summaries from biomedical articles. Two different datasets that have been created from curating two biomedical journals (PLOS and eLife) are provided by the task organizers. As a participant in this shared task, we developed a system to generate a lay summary from an article’s abstract and main text.Yayın A novel similarity based unsupervised technique for training convolutional filters(IEEE, 2023-05-17) Erkoç, Tuğba; Eskil, Mustata TanerAchieving satisfactory results with Convolutional Neural Networks (CNNs) depends on how effectively the filters are trained. Conventionally, an appropriate number of filters is carefully selected, the filters are initialized with a proper initialization method and trained with backpropagation over several epochs. This training scheme requires a large labeled dataset, which is costly and time-consuming to obtain. In this study, we propose an unsupervised approach that extracts convolutional filters from a given dataset in a self-organized manner by processing the training set only once without using backpropagation training. The proposed method allows for the extraction of filters from a given dataset in the absence of labels. In contrast to previous studies, we no longer need to select the best number of filters and a suitable filter weight initialization scheme. Applying this method to the MNIST, EMNIST-Digits, Kuzushiji-MNIST, and Fashion-MNIST datasets yields high test performances of 99.19%, 99.39%, 95.03%, and 90.11%, respectively, without applying backpropagation training or using any preprocessed and augmented data.Yayın TENET: a new hybrid network architecture for adversarial defense(Springer Science and Business Media Deutschland GmbH, 2023-08) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerDeep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these models are indeed extremely vulnerable to deliberately crafted and seemingly imperceptible perturbations of the input, referred to as adversarial examples. Adversarial attacks have the potential to substantially compromise the security of DNN-powered systems and posing high risks especially in the areas where security is a top priority. Numerous studies have been conducted in recent years to defend against these attacks and to develop more robust architectures resistant to adversarial threats. In this study, we propose a new architecture and enhance a recently proposed technique by which we can restore adversarial samples back to their original class manifold. We leverage the use of several uncertainty metrics obtained from Monte Carlo dropout (MC Dropout) estimates of the model together with the model’s own loss function and combine them with the use of defensive distillation technique to defend against these attacks. We have experimentally evaluated and verified the efficacy of our approach on MNIST (Digit), MNIST (Fashion) and CIFAR10 datasets. In our experiments, we showed that our proposed method reduces the attack’s success rate lower than 5% without compromising clean accuracy.Yayın Auto Train Brain increases the variance of the gamma band sample entropy in the left hemisphere in dyslexia: a pilot study(Springer Science and Business Media Deutschland GmbH, 2023) Eroğlu, GünetAuto Train Brain is a mobile app that improves reading speed and reading comprehension in dyslexia. The efficacy of Auto Train Brain was proven with a clinical trial. We have analyzed the long-term training effects of the Auto Train Brain on dyslexic children. We have collected QEEG data from 14 channels from 21 dyslexic children for 100 sessions and calculated the sample entropy in the gamma bands for the left posterior brain (T7, P7, and O1). Although the gamma band values fluctuate and no permanent increase in the gamma band values is detected after Auto Train Brain training at T7, P7, and O1, the variance of gamma band sample entropy increases as the neurofeedback session number increases. We have concluded that the Auto Train Brain increases the flexibility of the left brain in dyslexia.Yayın Mitosis detection using generic features and an ensemble of cascade adaboosts(Elsevier, 2013-05-30) Tek, Faik BorayContext: Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. Aims: We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object -level descriptions and thus require minimal segmentation. Materials and Methods: The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. Statistical Analysis Used: We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F -measure, and region overlap ratio measures. Results: We tested our features with two different classifier configurations: 1)An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non -cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F -measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape?based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. Conclusions: The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboosts performs better than the individual adaboost classifiers. Moreover, the ensemble of cascaded adaboosts was better than the ensemble of single adaboosts for mitosis detection.Yayın BOUN-ISIK participation: an unsupervised approach for the named entity normalization and relation extraction of Bacteria Biotopes(Association for Computational Linguistics (ACL), 2019-11-04) Karadeniz, İlknur; Tuna, Ömer Faruk; Özgu, ArzucanThis paper presents our participation at the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.Yayın Convolutional neural network (CNN) algorithm based facial emotion recognition (FER) system for FER-2013 dataset(IEEE, 2022-11-18) Ezerceli, Özay; Eskil, Mustafa TanerFacial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, and medical domains. Automated FER systems had been developed and have been used to recognize humans’ emotions but it has been a quite challenging problem in machine learning due to the high intra-class variation. The first models were using known methods such as Support Vector Machines (SVM), Bayes classifier, Fuzzy Techniques, Feature Selection, Artificial Neural Networks (ANN) in their models but still, some limitations affect the accuracy critically such as subjectivity, occlusion, pose, low resolution, scale, illumination variation, etc. The ability of CNN boosts FER accuracy. Deep learning algorithms have emerged as the greatest way to produce the best results in FER in recent years. Various datasets were used to train, test, and validate the models. FER2013, CK+, JAFFE and FERG are some of the most popular datasets. To improve the accuracy of FER models, one dataset or a mix of datasets has been employed. Every dataset includes limitations and issues that have an impact on the model that is trained for it. As a solution to this problem, our state-of-the-art model based on deep learning architectures, particularly convolutional neural network architectures (CNN) with supportive techniques has been implemented. The proposed model achieved 93.7% accuracy with the combination of FER2013 and CK+ datasets for FER2013.Yayın Analysis of single image super resolution models(IEEE, 2022-11-18) Köprülü, Mertali; Eskil, Mustafa TanerImage Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image superresolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.Yayın A comparison of Auto Train Brain neurofeedback rewarding interfaces in terms of efficacy(Acıbadem Mehmet Ali Aydınlar Üniversitesi, 2023-01-01) Eroğlu, GünetBackground/aim: Auto Train Brain is a mobile app that was specifically developed for dyslexic children to increase their reading speed and reading comprehension. In the original mobile app, only one unique neurofeedback user interface provided visually and audibly rewarding feedback to the subject with a red-green colored arrow on the screen. Later, new modules are added to the app with the end-users requests. These are the “youtube” video-based interface and “Spotify” auditory-based interface. In this research, we have compared the efficacy of the neurofeedback rewarding interfaces. Materials and methods: The experiment group consists of 20 dyslexic children aged 7-to 10 (15 males, 5 females) who were randomly assigned to one rewarding interface and used it at home for more than six months. Results: The result indicates that though the “youtube” interface is liked most by the participants, the arrow-based simple neurofeedback interface reduces theta brain waves more than other rewarding schemes. On the other hand, “youtube” and “Spotify” based interfaces increase Beta band powers more than the arrow interfaces in the cortex. The ”Spotify” user interface improves the fast brain waves more on the temporal lobes (T7 and T8) as the feedback given was only auditory. Conclusion: The results indicate that the relevant neurofeedback rewarding interface should be chosen based on the dyslexic child’s specific condition.Yayın A novel hybrid edge detection technique: ABC-FA(ISRES Organizasyon Turizm Eğitim Danışmanlık Ltd. Şti., 2017-11-09) Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, TuğrulImage processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods. The results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.Yayın Distribution games: a new class of games with application to user provided networks(Institute of Electrical and Electronics Engineers Inc., 2022-11-29) Taşçı, Sinan Emre; Shalom, Mordechai; Korçak, ÖmerUser Provided Network (UPN) is a promising solution for sharing the limited network resources by utilizing user capabilities as a part of the communication infrastructure. In UPNs, it is an important problem to decide how to share the resources among multiple clients in decentralized manner. Motivated by this problem, we introduce a new class of games termed distribution games that can be used to distribute efficiently and fairly the bandwidth capacity among users. We show that every distribution game has at least one pure strategy Nash equilibrium (NE) and any best response dynamics always converges to such an equilibrium. We consider social welfare functions that are weighted sums of bandwidths allocated to clients. We present tight upper bounds for the price of anarchy and price of stability of these games provided that they satisfy some reasonable assumptions. We define two specific practical instances of distribution games that fit these assumptions. We conduct experiments on one of these instances and demonstrate that in most of the settings the social welfare obtained by the best response dynamics is very close to the optimum. Simulations show that this game also leads to a fair distribution of the bandwidth.
