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Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks(IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico IIIn this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.Publication Metadata only On the Structural Code Clone Detection Problem: A Survey and Software Metric Based Approach(SPRINGER-VERLAG BERLIN, 2014) Kapdan, Mustafa; Aktas, Mehmet; Yigit, Melike; Murgante, B; Misra, S; Rocha, AMAC; Torre, C; Rocha, JG; Falcao, MI; Taniar, D; Apduhan, BO; Gervasi, O; Turkish Airlines; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Yildiz Technical University; Bahcesehir UniversityUnnecessary repeated codes (clones) have not been well documented and are difficult to maintain. Code clones may become an important problem in software development cycle and they must be fixed in all occurrences. This condition increases significantly software maintenance costs and required effort/duration for understanding the code. Over the years, many techniques have been proposed in order to minimize or prevent the code cloning problems. The main focus of these techniques is on the detection of clones. In such studies, code cloning is studied under two main categories: simple and structural. Simple clone is defined as the similarity that arises from the repetition of the code snippet in the software. Structural clone is defined as the similarity in software structure (i.e. design patterns and object oriented programming class relations). Simple clone detection techniques fail to determine the reasons of code repetition whether it is due to design or not, as they do not look at the code from a wider perspective for repetitive code snippets. In this study, we survey the existing structural clones approaches. We also introduce an approach that utilizes software quality metrics for detecting the structural code clones.Publication Metadata only Optimizing the rate of a correlated MIMO link jointly over channel estimation and data transmission parameters(IEEE, 2008) Soysal, Alkan; Ulukus, Sennur; Bahcesehir University; University System of Maryland; University of Maryland College ParkWe consider a single-user correlated MIMO channel with block fading, where each block is divided into training and data transmission phases. The receiver has a noisy CSI that it obtains through a channel estimation process, while the transmitter has partial CSI in the form of covariance feedback. We optimize the achievable rate jointly over the parameters of the training and data transmission phases. In particular, we first choose the training signal to minimize the channel estimation error, and then, develop an iterative algorithm to solve for the optimum training duration, the optimum allocation of power between training and data transmission phases, and the optimum allocation of power over the antennas during the data transmission phase.Publication Metadata only An Elliptic Curve Cryptographic Processor Using Edwards Curves and the Number Theoretic Transform(SPRINGER-VERLAG BERLIN, 2015) Mentens, Nele; Batina, Lejla; Baktir, Selcuk; Ors, B; Preneel, B; KU Leuven; Interuniversity Microelectronics Centre; Radboud University Nijmegen; Bahcesehir UniversityHardware implementations of ECC processors based on Edwards curves are very useful for various applications of security due to the regularity of point operations. In this paper we explore one such direction taking advantage of the DFT modular multiplication in a special composite field of a prime characteristic. Our results show potential in terms of compactness while maintaining a feasible latency. We expect this approach to be more beneficial for side-channel security.Publication Metadata only A Holistic Methodology for Digital Governance Transition and Measuring Its Effect on Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansDigital transformation has become one of the most emphasized issues for organizations today. Organizations have a long way to go in this difficult road. First of all, they should develop and measure their governance skills, and then they need to create the necessary studies in both technological and managerial terms for IT systems to become a value. While doing all these, they should also create a risk management structure with a correct approach by performing consultancy, supervision, and evaluation services completely. In line with this whole process, there are different standards and practices in every field they need to follow. This paper presents a methodology proposition in a general perspective that can be applied end-to-end in the field of digitalization, starting from the governance structure to the IT value-adding strategy. Thanks to this new methodology, it is possible to see many processes such as governance, consultancy, digital transformation, auditing, internal control, which are disconnected from each other, in the big picture and arrange all activities accordingly. With long-term follow-up, the strategic and tactical perspectives of institutions can be created and their progress over time can be followed by this new methodology. This paper performs both qualitative and quantitative measurements of the applications of the new methodology to compare them with existing ones. This methodology introduces a holistic methodology lacking in the market.Publication Metadata only Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms(ASSOC COMPUTING MACHINERY, 2019) Aslan, Sinem; Alyuz, Nese; Tanriover, Cagri; Mete, Sinem E.; Okur, Eda; D'Mello, Sidney K.; Esme, Asli Arslan; Intel Corporation; Intel USA; Bahcesehir University; University of Colorado System; University of Colorado BoulderWe developed a real-time, multimodal Student Engagement Analytics Technology so that teachers can provide just-in-time personalized support to students who risk disengagement. To investigate the impact of the technology, we ran an exploratory semester-long study with a teacher in two classrooms. We used a multi-method approach consisting of a quasi-experimental design to evaluate the impact of the technology and a case study design to understand the environmental and social factors surrounding the classroom setting. The results show that the technology had a significant impact on the teacher's classroom practices (i.e., increased scaffolding to the students) and student engagement (i.e., less boredom). These results suggest that the technology has the potential to support teachers' role of being a coach in technology-mediated learning environments.Publication Metadata only A Research for Measuring the Effects of COVID-19 on Digital Transformation within Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansPandemics are not only medical phenomenon, but they also influence people and society in many respects. It has an effect on almost all markets all over the world. COVID-19 pandemic, expressed as change, empowerment, or post-traumatic growth, with several negative consequences as well as positive consequences. It also has the potential for opportunities. Years of change in the way companies do business have resulted from the COVID 19 crisis across all industries and regions. The aim of this research is to examine the relationship between different demographic variables, COVID-19's impact on digital transformation and post-traumatic effects. The article reflects in practical terms on whether and how the COVID-19 emergence in organizations accelerates digital transformation. This study is a descriptive quantitative approach research based on general survey model.Publication Metadata only Online Naive Bayes Classification for Network Intrusion Detection(IEEE, 2014) Gumus, Fatma; Sakar, C. Okan; Erdem, Zeki; Kursun, Olcay; Wu, X; Ester, M; Xu, G; Istanbul University; Bahcesehir University; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)Intrusion detection system (IDS) is an important component to ensure network security. In this paper we build an online Naive Bayes classifier to discriminate normal and bad (intrusion) connections on KDD 99 dataset for network intrusion detection. The classifier starts with a small number of training examples of normal and bad classes, then, as it classifies the rest of the samples one at a time, it continuously updates the mean and the standard deviations of the features (IDS variables). We present experimental results of parameter updating methods and their parameters for the online Naive Bayes classifier. The obtained results show that our proposed method performs comparably to the simple incremental update.Publication Metadata only Multivariate Spatio-temporal Cellular Traffic Prediction with Handover Based Clustering(IEEE, 2022) Tuna, Evren; Soysal, Alkan; Turkcell Turkey; Bahcesehir University; Virginia Polytechnic Institute & State UniversityWe consider an RNN-based traffic volume prediction, which is a critical problem for network slice management and resource allocation in slicing-enabled next generation cellular networks. We propose to use a novel cost function that takes SLA violations into account. Our approach is multivariate and spatio-temporal in three aspects. First, we consider the effects of several other RAN features in a cell besides the traffic volume. Second, we introduce feature vectors based on peak hours of the day and days of the week. Third, we introduce feature vectors based on incoming handover statistics from the neighboring cells. Our results show about 60% improvement over MAE-based univariate LSTM models and about 20% improvement over SLA-based univariate models.