• An architecture viewpoint for modeling dynamically configurable software systems 

  Tekinerdogan, B.; Sözer, Hasan (Elsevier, 2017-01-01)

  Current software systems are rarely static and need to be able to change their topology and behavior to the changing context. To support the communication among stakeholders, guide the design decisions, and analyze the ...

• Networking standards 

  Beğen, Ali Cengiz; Bök, P. B.; Saltsidis, P. (IEEE, 2017-03)

  The article in this special section focus on the market for new networking technologies. Networking technologies are advancing faster than ever before. Aspects driving this change in velocity is the need to support faster, ...

• Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks 

  Narmanlıoğlu, Ö.; Zeydan, E.; Kandemir, Melih; Kranda, T. (IEEE, 2017-01-01)

  Internet-empowered electronic gadgets and content rich multimedia applications have expanded exponentially in recent years. As a consequence, heterogeneous network structures introduced with Long Term Evolution (LTE) ...

• Networking standards 

  Beğen, Ali Cengiz; Bok, P. B.; Saltsidis, P. (IEEE, 2017-12)

  N/A

• From capturing to rendering: Volumetric media delivery with six degrees of freedom 

  Van Der Hooft, J.; Vega, M. T.; Wauters, T.; Timmerer, C.; Beğen, Ali Cengiz; De Turck, F.; Schatz, R. (IEEE, 2020-10)

  Technological improvements are rapidly advancing holographic-type content distribution. Significant research efforts have been made to meet the low latency and high bandwidth requirements set forward by interactive ...

• OzU-NLP at TREC NEWS 2019: Entity ranking 

  Fayoumi, Kenan; Yeniterzi, R. (National Institute of Standards and Technology (NIST), 2019)

  This paper presents our work and submission for TREC 2019 News Track: Entity Ranking Task. Our approach utilizes Doc2Vec's ability to represent documents as fixed sized numerical vectors. Applied on news articles and ...

• Sampling-free variational inference of bayesian neural networks by variance backpropagation 

  Haußmann, M.; Hamprecht, F. A.; Kandemir, Melih (ML Research Press, 2020)

  We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ...

• ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing 

  Akbulut, M. T.; Öztop, Erhan; Xue, H.; Tekden, A. E.; Şeker, M. Y.; Uğur, E. (ML Research Press, 2020)

  To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). ...

• Uncertainty-aware deep classifiers using generative models 

  Şensoy, Murat; Kaplan, L.; Cerutti, F.; Saleki, Maryam (Association for the Advancement of Artificial Intelligence, 2020)

  Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...

• A benchmark for inpainting of clothing images with irregular holes 

  Kınlı, Osman Furkan; Özcan, Barış; Kıraç, Mustafa Furkan (Springer, 2020)

  Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has ...

• Exploring scaling efficiency of intel loihi neuromorphic processor 

  Uludağ, Recep Buğra; Çaǧdaş, S.; Işler, Y. S.; Şengör, N. S.; Aktürk, İsmail (IEEE, 2023)

  In this paper, we focus on examining how scaling efficiency evolves in winner-take-all (WTA) network models on Intel Loihi neuromorphic processor, as network-related features such as network size, neuron type, and connectivity ...

• Effects of agent's embodiment in human-agent negotiations 

  Çakan, Umut; Keskin, Mehmet Onur; Aydoǧan, Reyhan (ACM, 2023-09-19)

  Human-agent negotiation has recently attracted researchers’ attention due to its complex nature and potential usage in daily life scenarios. While designing intelligent negotiating agents, they mainly focus on the interaction ...

• Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources 

  Barakat, Huda Mohammed Mohammed; Turk, O.; Demiroğlu, Cenk (Springer, 2024-02-12)

  Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high ...

• Towards interactive explanation-based nutrition virtual coaching systems 

  Buzcu, Berk; Tessa, M.; Tchappi, I.; Najjar, A.; Hulstijn, J.; Calvaresi, D.; Aydoğan, Reyhan (Springer, 2024-01)

  The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition ...

• Discovering predictive relational object symbols with symbolic attentive layers 

  Ahmetoglu, A.; Celik, B.; Öztop, Erhan; Uğur, E. (IEEE, 2024-02-01)

  In this letter, we propose and realize a new deep learning architecture for discovering symbolic representations for objects and their relations based on the self-supervised continuous interaction of a manipulator robot ...

• Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework 

  Taliaronak, V.; Lange, A. L.; Kırtay, M.; Öztop, Erhan (IEEE, 2023)

  Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective - that is, forming robot trust in interaction partners. Our previous ...

• EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things 

  Kashif, Muhammad; Çakmakçı, Kübra Kalkan (Elsevier, 2024-01)

  With the increasing popularity of the Internet of things (IoT) and giving the end users the opportunity of collecting and analyzing the data by these IoT devices give rise to ultimate privacy concern and is attracting ...

• Feature extraction for enhancing data-driven urban building energy models 

  Bolluk, Muhammed Said; Seyis, Senem; Aydoğan, Reyhan (European Council on Computing in Construction (EC3), 2023)

  Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information ...

• Towards test automation for certification tests in the banking domain 

  Elakas, A.; Tarlan, Ozan; Safak, I.; Çakmakçı, Kübra Kalkan; Sözer, Hasan (IEEE, 2023)

  Software systems in the banking domain are business-critical applications that provide financial services. These systems are subject to rigorous certification tests, which are performed manually, and take weeks to complete. ...

• Point of sale Fraud detection methods via machine learning 

  Begen, E.; Sayan, İ. U.; Bayrak, A. T.; Yıldız, Olcay Taner (IEEE, 2023)

  Restaurant cash registers frequently experience fraudulent transactions, leading to substantial financial losses for operators. Despite several methods aimed at preventing fraud at the cash register, addressing this issue ...

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