Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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Publication An Efficient Generation and Security Analysis of Substitution Box Using Fingerprint Patterns(IEEE, 2020) ŞENGEL, ÖZNUR; Aydın, Muhammed Ali; Sertbaş, AhmetInformation and its security have attracted the research community in recent years with increasing usage of mobile applications. Mobile devices have different security options in data transmission such as reading some biometric values. The keystone of the modern block and stream ciphers is the use of a substitution box (s-box) that obscures correlation between plaintext and ciphertext. In this study, we proposed a novel s-box generation algorithm by using the fingerprint pattern of the person who transfers information to the target. We generated several s-boxes by using bifurcation and ridge ending features of the fingerprint. Proposed s-boxes are compared with several known s-boxes over nonlinearity, bijectiveness, strict avalanche criterion, bit independence criterion, linear probability, and differential probability. Along with these properties, we analyzed confidence interval and randomness properties of new s-boxes as well. Also, the execution time of the proposed s-box generation algorithm is calculated and examined. The results of the cryptographic properties have shown that the proposed s-boxes by using ridge ending of the fingerprint performs better. The performances analysis show that the proposed s-box has satisfactory results according to the results of chaotic-based s-boxes. On the other hand, the fingerprint s-boxes are much better than the existing biometric s-boxes according to the s-box security metrics. The results have shown that the execution time of the proposed s-box generation algorithm is more minimum than the existing biometric s-box generation algorithms. Resulting from applying fingerprint biometric data to generate an s-box, such a successful algorithm is promising to be used in mobile devices.Publication Analysis of Facial Emotion Expression in Eating Occasions Using Deep Learning(Springer, 2023) ELİF, YILDIRIM; AKBULUT, FATMA PATLAR; Çatal, ÇağatayEating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.Publication AT-ODTSA: A Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis(University of Bahrain, 2022) Sahmoud, Shaaban; Abudalfa, Shadi; ELMASRY, WİSAMIn the field of sentiment analysis, most of research has conducted experiments on datasets collected from Twitter for manipulating a specific language. Little number of datasets has been collected for detecting sentiments expressed in Arabic tweets. Moreover, very limited number of such datasets is suitable for conducting recent research directions such as target dependent sentiment analysis and open-domain targeted sentiment analysis. Thereby, there is a dire need for reliable datasets that are specifically acquired for open-domain targeted sentiment analysis with Arabic language. Therefore, in this paper, we introduce AT-ODTSA, a dataset of Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets (topics) and sentiments (opinions) expressed in the collected tweets. To the best of our knowledge, our work presents the first dataset that manually annotated for applying Arabic open-domain targeted sentiment analysis. We also present a detailed statistical analysis of the dataset. The AT-ODTSA dataset is suitable for train numerous machine learning models such as a deep learning-based model. © 2022 University of Bahrain. All rights reserved.Publication Blockchain-Based KYC Model for Credit Allocation in Banking(IEEE-Inst Electrical Electronics Engineers Inc., 2024) Karadağ, Bulut; Zaim, A. Halim; AKBULUT, AKHANThe implementation of the Know Your Customer (KYC) strategy by banks within the financial sector enhances the operational efficiency of such establishments. The data gathered from the client during the KYC procedure may be applied to deter possible fraudulent activities, money laundering, and other criminal undertakings. The majority of financial institutions implement their own KYC procedures. Furthermore, a centralized system permits collaboration and operation execution by multiple financial institutions. Aside from these two scenarios, KYC processes can also be executed via a blockchain-based system. The blockchain's decentralized network would be highly transparent, facilitating the validation and verification of customer data in real-time for all relevant stakeholders. In addition, the immutability and cryptography of the blockchain ensure that client information is secure and immutable, thereby eradicating the risk of data breaches. Blockchain-based KYC can further improve the client experience by eliminating the requirement for redundant paperwork and document submissions. After banks grant consumers loans, a blockchain-based KYC system is proposed in this study to collect limit, risk, and collateral information from them. The approach built upon Ethereum grants financial institutions the ability to read and write financial data on the blockchain network. This KYC method establishes a transparent, dynamic, and expeditious framework among financial institutions. In addition, solutions are discussed for the Sybil attack, one of the most severe problems in such networks.Publication Boosting the Visibility of Services in Microservice Architecture(Springer, 2023) TOKMAK, AHMET VEDAT; AKBULUT, AKHAN; Çatal, ÇağatayMonolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.Publication A Clustering Approach for Intrusion Detection with Big Data Processing on Parallel Computing Platform(Bajece (İstanbul Teknik Üniversitesi), 2019) REİS, B.; KAYA, S. B.; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years there is a growing number of attacks in the computer networks. Therefore, the use of a prevention mechanism is an inevitable need for security admins. Although firewalls are preferred as the first layer of protection, it is not sufficient for preventing lots of the attacks, especially from the insider attacks. Intrusion Detection Systems (IDSs) have emerged as an effective solution to these types of attacks. For increasing the efficiency of the IDS system, a dynamic solution, which can adapt itself and can detect new types of intrusions with a dynamic structure by the use of learning algorithms is mostly preferred. In previous years, some machine learning approaches are implemented in lots of IDSs. In the current position of artificial intelligence, most of the learning systems are transferred with the use of Deep Learning approaches due to its flexibility and the use of Big Data with high accuracy. In this paper, we propose a clustered approach to detect the intrusions in a network. Firstly, the system is trained with Deep Neural Network on a Big Data set by accelerating its performance with the use of CUDA architecture. Experimental results show that the proposed system has a very good accuracy rate and low runtime duration with the use of this parallel computation architecture. Additionally, the proposed system needs a relatively small duration for training the system.Publication A Comparative Assessment of Five Different Distributions Based on Five Different Optimization Methods for Modeling Wind Speed Distribution(Gazi University, 2023) Wadi, Mohammed; ELMASRY, WİSAMDetermining wind regime distribution patterns is essential for many reasons; modelling wind power potential is one of the most crucial. In that regard, Weibull, Gamma, and Rayleigh functions are the most widely used distributions for describing wind speed distribution. However, they could not be the best for describing all wind systems. Also, estimation methods play a significant role in deciding which distribution can achieve the best matching. Consequently, alternative distributions and estimation methods are required to be studied. An extensive analysis of five different distributions to describe the wind speeds distribution, namely Rayleigh, Weibull, Inverse Gaussian, Burr Type XII, and Generalized Pareto, are introduced in this study. Further, five metaheuristic optimization methods, Grasshopper Optimization Algorithm, Grey Wolf Optimization, Moth-Flame Optimization, Salp Swarm Algorithm, and Whale Optimization Algorithm, are employed to specify the optimum parameters per distribution. Five error criteria and seven statistical descriptors are utilized to compare the good-of-fitness of the introduced distributions. Therefore, this paper provides different important methods to estimate the wind potential at any site..Publication Deep Learning-Based Defect Prediction for Mobile Applications(MPDI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, AlokSmartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.Publication Deep Learning-Based User Experience Evaluation in Distance Learning(Springer, 2023) SADIGOV, RAHIM; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR; Çatal, ÇağatayThe Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.Publication Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini(Bitlis Eren Üniversitesi Rektörlüğü, 2020) Şişmanoğlu, Gözde; Koçer, Furkan; Önde, Mehmet ali; ŞAHİNGÖZ, ÖZGÜR KORAYSon yıllarda, bilgisayarların donanımındaki teknolojik gelişmeler ve makine öğrenme tekniklerindeki gelişmeler nedeniyle, "Büyük Veri" ve "Paralel İşleme" kullanımı olmak üzere problem çözmek için iki artan yaklaşım vardır. Özellikle GPU'lar gibi çok çekirdekli bilgi işlem aygıtlarında paralel olarak gerçekleştirilebilen Derin Öğrenme algoritmalarının ortaya çıkmasıyla, bu yaklaşımlarla birçok gerçek dünya problemleri çözülebilmektedir. Derin öğrenme modelleri eğitildikleri veri ile sınıflandırma, regresyon analizi ve zaman serilerinde tahmin gibi uygulamalarda büyük başarılar göstermektedir. Bu modellerin finansal piyasadaki en aktif uygulama alanlarından biri özellikle borsada işlem gören hisse senetlerinin tahmini işlemleridir. Bu alanda amaç, pazardaki değişim süreci hakkındaki hisse senedinin önceki günlük verilerine bakarak kısa veya uzun vadeli gelecekteki değerini tahmin etmeye çalışmaktır. Bu çalışmada, LSTM, GRU ve BLSTM isimli 3 farklı derin öğrenme modeli kullanılarak bir hisse senedi tahmin sistemi geliştirilip, kullanılan modeller arasında karşılaştırmalı bir analiz yapıldı. Spekülatif hareketlerden uzak olması için veri seti olarak 1968'den 2018'e kadar olan New York Borsası'ndan hisse senedinin zaman serisi değerlerini kullanıldı. Spesifik olarakta IBM hisse senedi ile test çalışmaları yapıldı. Deneysel sonuçlar BLSTM modelinin 5 günlük girdi verileriyle eğitilmesi ile %63,54 lük bir yönsel doğruluk değerine ulaşıldığını göstermektedir.Publication Design and Implementation of a Deep Learning-Empowered m-Health Application(Springer, 2023) AKBULUT, AKHAN; DESOUKI, SARA; ABDELKHALIQ, SARA; KHANTOMANI, LAYAL; Çatal, ÇağatayMany people are unaware of the severity of melanoma disease even though such a disease can be fatal if not treated early. This research aims to facilitate the diagnosis of melanoma disease in people using a mobile health application because some people do not prefer to visit a dermatologist due to several concerns such as feeling uncomfortable by exposing their bodies. As such, a skincare application was developed so that a user can easily analyze a mole at any part of the body and get the diagnosis results quickly. In the first phase, the corresponding image is extracted and sent to a web service. Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. The final phase displays the confidence rates on the mobile application. The proposed model utilizes the Convolutional Neural Network and provides 84% accuracy and 72% precision. The results demonstrate that the proposed model and the corresponding mobile application provide remarkable results for addressing the specified health problem.Publication A Design of an Integrated Cloud-Based Intrusion Detection System with Third Party Cloud Service(Walter de Gruyter GmbH, 2021) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul HalimAlthough cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature. © 2021 Wisam Elmasry et al., published by De Gruyter 2021.Publication Detection of Phishing Websites from URLs by using Classification Techniques on WEKA(IEEE, 2021) GEYİK, BÜŞRA; ERENSOY, KÜBRA; Koçyiğit, EmreThe Internet is getting stronger day by day and it makes our lives easier with many applications that are executed on cyberworld. However, with the development of the internet, cyber-attacks have increased gradually and identity thefts have emerged. It is a type of fraud committed by intruders by using fake web pages to access people's private information such as userid, password, credit card number and bank account numbers, etc. These scammers can also send e-mail from many important institutions and organizations by using phishing attacks which imitate these web pages and acts as if they are original. Traditional security mechanisms can not prevent these attacks because they directly target the weakest part of connection : end-users. Machine learning technology has been used to detect and prevent this type of intrusions. The anti-phishing method has been developed by detecting the attacks made with the technologies used. In this paper, we combined the websites used by phishing attacks into a dataset, then we obtained some results using 4 classification algorithms with this dataset. The experimental results showed that the proposed systems give very good accuracy levels for the detection of these attacks.Publication Evaluating the Effects of the Autonomic Nervous System and Sympathetic Activity on Emotional States(Doç. Dr. Necip Şimşek, 2022) AKBULUT, FATMA PATLAREmotion recognition has attracted more interest by being applied in many application areas from different domains such as medical diagnosis, e-commerce, and robotics. This research quantifies the stimulated short-term effect of emotions on the autonomic nervous system and sympathetic activity. The primary purpose of this study is to investigate the responses of 21 adults by attaching a wearable system to measure physiological data such as an electrocardiogram and electrodermal activity in a controlled environment. Cardiovascular effects were evaluated with heart rate variability indices that included HR, HRV triangular-index, rMSSD (ms), pNN5O (%); frequency analysis of the very low frequency (VLF: 0-0,04 Hz), low frequency (LF: 0,04-0,15 Hz), and high frequency (HF: 0,15-0,4 Hz) components; nonlinear analysis. The sympathetic activity was evaluated with time-varying and time-invariant spectral analysis results of the EDA. The participants who experience calmness had a 4,8% lower heart rate (75,06±16,76 and 78,72±16,52) observed compared to happiness. Negative valance with high-arousal emotions like anger was invariably responded to with a peak in skin conductance level. Besides, negative valance with low-arousal emotions like sadness was allied with a drop in conductance level. Anger, in addition to being the most well-known emotion, elicited coherent time-varying spectral responses.Publication An Evolutionary Approach to Multiple Traveling Salesman Problem for Efficient Distribution of Pharmaceutical Products(Institute of Electrical and Electronics Engineers Inc., 2020) Koçyiğit, Emre; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, BanuConsiderable growth of computer science has created novel solutions for variable problem fields and has increased the efficiency of available solutions. Evolutionary algorithms are quite successful in dealing with real-world problems that require optimization. In this article, we implemented a Genetic Algorithm that is well known evolutionary algorithm in order to provide an efficient solution for the Distribution of Pharmaceutical Products, which is a vital optimization problem, especially in situations such as a pandemic. The Multiple Traveling Salesman Problem approach was used to distribute pharmaceutical products as soon as possible. Moreover, we strengthened our proposal algorithm with 2-Opt Algorithm to get optimal results in earlier iterations. Different datasets from a library were applied to measure the quality of solutions and computation time. At the end of the work, we observed that our proposed algorithm generates successful solutions in an acceptable running time. This study will be extended with a new mutation concept as future work.Publication Hayalet Uzuv Sendromu Tedavisi için Sanal Gerçeklik ve Artırılmış Gerçeklik Temelli Sistemin Geliştirilmesi(TÜBİTAK EEEAG Proje, 2020) AKBULUT, AKHAN; Zaim, Abdül Halim; Aydın, Ali; Tarakcı, ElaHayalet uzuv sendromu (Fantom ekstremite ağrısı-FEA), ampütasyon sonrasında bireylerin birçoğunda görülen ve yaşam kalitesini azaltarak hayatlarını olumsuz yönde etkileyen yaygın bir ampütasyon sekelidir. Kaybedilen uzvun beyinde temsil edildiği kortikal alanların, uzuv kaybından dolayı duyusal girdiden yoksun kalması ve komşu duyusal girdilere açık hale gelmesinin kayıp uzuv ile ilgili ağrılı temsillere neden olduğu öne sürülmektedir. FEA'yı tedavi etmeye yönelik birçok farklı uygulama bulunmakla birlikte; etkinliği en fazla gösterilen ve en yaygın kullanılan terapötik yaklaşım ayna terapisidir. Ayna terapisi, sağlam ekstremite ile yapılan hareketlerin yansıma aldatmacasını kullanarak kayıp uzvu beyine varmış gibi gösterip, ağrının azaltılarak bireyin rahatlatılmasını hedeflemektedir. Proje kapsamında, benzer bakış açısıyla ve ayna terapisinin limitasyonlarını ortadan kaldırarak FEA'nın rehabilitasyonunda kullanılmak üzere, 4 farklı ampütasyon bölgesi için sanal gerçeklik ve artırılmış gerçeklik teknolojilerinin yer aldığı 7 (4 SG, 3 AG) oyun geliştirilmiş; katılımcıların ampüte bölgelerinden ölçülen EMG sinyallerinin karşılığı olan fantom hareketler belirlenerek interaktif oyunlar içerisindeki modellere yansıtılması yöntemi ile rehabilitasyon seansları gerçekleştirilmiştir. Fantom hareketlerin yüksek doğrulukla sınıflandırılması için 71 kişiden toplanan özgün bir veriseti oluşturulmuş ve sistemin kullandığı yapay öğrenme modelinin eğitilmesinde kullanılmıştır. Farklı yapay öğrenme algoritmaları ile yapılan deneylerde en yüksek başarımı sunan modeller, ilk örnekleme entegre edilmiş ve sistem %88,94?e varan doğrulukla hareketleri sınıflandırmıştır. Proje konusu, fizyolojik sinyallerin ölçümünde gündelik hareketleri etkilemeden kullanılabilecek giyilebilir bir sensör cihazın geliştirilmesi, sağlık verisinin az kaynak tüketerek güvenli bir şekilde aktarılması ve fizyoterapistlerin hasta takibini yapabileceği web uygulamalarının geliştirmesini de kapsamaktadır. Önerilen sistemin ilk örneklemi 12 hasta ile test edilmiş; yapılan kullanım analizi ve geribildirimler neticesinde sistemin ampüte bireyler için kontrol edilebilir, doğal, eğlenceli, dalma seviyesi yüksek, fantom ekstremiteyi hareket ettirmelerini sağlayan, kalan uzuvdaki kasların çalışmasına katkıda bulunan ve kassal yorgunluk oluşturmayan bir rehabilitasyon aracı olabileceği, iyi bir değerlendirme sonrasında sistemin kullanımı ile ilgili herhangi bir şikayeti olmayan ampüte bireyler tarafından rahatlıkla kullanılabileceği ve yüksek memnuniyet düzeyine sahip olduğu görülmüştür.Publication Identification of Phantom Movements With an Ensemble Learning Approach(Pergamon-Elsevier Science Ltd., 2022) AKBULUT, AKHAN; Güngör, Feray; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdul Halim; Çatal, ÇağatayPhantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.Publication Investigating the Effects of Stress on Achievement: BIOSTRESS Dataset(Elsevier Inc., 2023) ÇÖPÜRKAYA, ÇAĞLA; MERİÇ, ELİF; ERİK, ELİF BERRA; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR; Çatal, ÇağatayThe effects of chronic stress on academic and professional achievement can have a substantial impact. This relationship is highlighted through a dataset that includes questionnaires and physiological data from a group of individuals. The questionnaire data of 48 individuals, the physiological data of 20 individuals during sessions with a psychologist, and the exam data of 8 individuals were analyzed. The questionnaire data collected includes demographic information and scores on the TOAD stress scale. Physiological data was captured using the Empatica e4, a wearable device, which measured various signals such as blood volume pulse, electrodermal activity, body temperature, interbeat intervals, heart rate, and 3-axis accelerometer data. These measurements were taken under different stress conditions, both high and low, during therapy sessions and an exam respectively. Overall, this study significantly contributes to our understanding of how stress affects achievement. By providing a large dataset consisting of questionnaires and physiological data, this research helps researchers gain a better understanding of the complex relationship between stress and achievement. It also enables them to develop innovative strategies for managing stress and enhancing academic and professional success.Publication LWE: An Energy-Efficient Lightweight Encryption Algorithm for Medical Sensors and IoT Devices(Istanbul University, 2020) Toprak, Sezer; AKBULUT, AKHAN; Aydın, Muhammet Ali; Zaim, Abdul HaimIn today's world, systems generate and exchange digital data frequently and face a much broader range of threats than in the past. Within the context of this unsafe ecosystem, it is crucial to protect the data in a quick and secure way. In this paper, it is proposed that a lightweight block cipher algorithm called LWE in the purpose of having an encryption algorithm that is light enough for restricted/limited hardware environments and secure enough to endure primal cryptanalysis attacks. The length of blocks to be encrypted is set to 64 bits and the key length is defined as 64 bits. It is targeted for IoT systems with low-end microcontrollers and body sensor area devices. The performance and security aspects of LWE are evaluated with well-known algorithms and it is observed that LWE can establish a basic security baseline for transmitting raw data without creating a heavy load on the network infrastructure.Publication Mac Lane's Comparison Theorem for the Kleisli Construction Formalized in Coq(Springer, 2020) EKİCİ, BURAK; Kaliszyk, Cezary(co)Monads are used to encapsulate impure operations of a computation. A (co)monad is determined by an adjunction and further determines a specific type of adjunction called the (co)Kleisli adjunction. Mac Lane introduced the comparison theorem which allows comparing these adjunctions bridged by a (co)monad through a unique comparison functor. In this paper we specify the foundations of category theory in Coq and show that the chosen representations are useful by certifying Mac Lane's comparison theorem and its basic consequences. We also show that the foundations we use are equivalent to the foundations by Timany. The formalization makes use of Coq classes to implement categorical objects and the axiom uniqueness of identity proofs to close the gap between the contextual equality of objects in a categorical setting and the judgmental Leibniz equality of Coq. The theorem is used by Duval and Jacobs in their categorical settings to interpret the state effect in impure programming languages.