Completed Projects

The projects listed below have been completed at Koc University after the KUIS AI.

An Information-Theoretic Framework for Self-Supervised Learning

Funded by: Google
Dates: 2021-2022
Principal Investigators: A. Erdoğan and D. Yuret

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Engaging Humans in Gamified Memory Training Using Humanoid Robots

Funded by: STINT
Dates: 2021-2022
Researchers: R.Lowe (PI) and E. Erzin, M. Sezgin, Y. Yemez

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Tracing the Ruin: Modelling the Collapse Process of Ancient Structures at Sagalassos (Ağlasun, Burdur)

Funded by: Koç University Seed Fund
Dates: 2020-2022
Researchers: I. Uytterhoeven (PI), F
. Güney

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As a proof of concept this project, proposed by Assoc. Prof. Dr. Inge Uytterhoeven (Department of Archaeology and History of Art) in collaboration with Assist. Prof. Dr. Fatma Güney (Department of Computer Science and Engineering), intends to model the collapse of ancient structures caused by earthquakes, combining research approaches of Archaeology, Architecture, Computer Engineering, Archaeoseismology, Conservation, and Cultural Heritage, and taking the archaeological site of Sagalassos (Ağlasun, Burdur) as a test case. The project aims to develop a large number of realistic simulations of the distortion, displacement and tumbling down of building elements of a set of ancient structures with different architectural characteristics at Sagalassos. In this way, it intends to offer an innovative methodology to learn the dynamics of physics, causing the collapse in seismic calamities. Moreover, we hope to discriminate between various seismic events that may have followed each other through time, as well as to distinguish between earthquake damage and other processes of structural decay that impacted ancient structures, including the salvaging of building materials for recycling purposes or natural gradual processes of decay. Furthermore, the simulations aim to contribute to the fields of conservation and anastylosis by giving insights into the position, orientation, and extent of collapsed building elements in relation to the structures they belonged to, and into the impact of future earthquakes on rebuilt structures. Finally, the project aims to contribute to the visualisation for the broad public of the effects of seismic activity on ancient urban societies.

Tangible Intelligent Interfaces for Teaching Computational Thinking Skills

Funded by: the Scientific and Technological Research Council of Türkiye – TÜBİTAK
Dates: 2020-2022
Researchers: M. Sezgin (PI), A. Sabuncuoğlu

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Volume Maximization and Minimization Based Regularization for Deep Neural Networks

Funded by: Google LLC.
Dates: 2020-2021
Principal Investigators: A. Erdoğan and D. Yuret

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The New Politics of Welfare: Towards an “Emerging Markets

Funded by: European Research Council
Dates: 2017-2021
Principal Investigator: E. Yörük and D. Yuret

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Can we say that emerging market economies are developing a new welfare regime? If so, what has caused this?

This project has two hypotheses:
Hypothesis 1: China, Brazil, India, Indonesia, Mexico, South Africa and Türkiye are forming a new welfare regime that differs from liberal, corporatist and social democratic welfare regimes of the global north on the basis of expansive, and decommodifying social assistance programmes for the poor.
Hypothesis 2: This new welfare regime is emerging principally as a response to the growing political power of the poor as a dual source of threat and support for governments.
The project challenges and expands the state-of-the-art in three different literatures by developing novel concepts and approaches:

  1. Welfare regimes (by adding a new type of welfare regime – Hypothesis 1),
  2. Welfare-state development (by re-establishing the centrality of political factors– Hypothesis 2)
  3. Contentious politics (by showing that welfare policy changes are by-products of contemporary contentious politics – Hypothesis 2).

Effortless Parallelization of Deep Learning Frameworks

Funded by: the Scientific and Technological Research Council of Türkiye – TÜBİTAK
Dates: 2019-2021
Principal Investigator: D. Unat

The aim of the project is to perform effortless parallelization on deep neural networks. Hybrid-parallel approaches that blend the data and the model, especially the model-parallel approach, will be applied automatically on the model and various program improvements will be performed. The project will develop a series of optimization techniques that will enable the user to use the devices in the current hardware system efficiently without any code changes according to the topology and the structure of the deep neural network trained. Suggested improvements will be implemented on popular deep learning frameworks such as TensorFlow and MXNet, which illustrate deep neural network models as a data-flow graph.

Investigation of Friction Between Human Finger and Surface of a Capacitive Touch Screen Actuated by Electrostatic Forces for Haptic Feedback

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 2018-2021
Principal Investigator: Ç. Başdoğan

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Capacitive touch screens are indispensable part of smart phones, tablets, kiosks, and laptop computers in nowadays. They are used to detect our finger position and enable us to interact with text, images, and data displayed by the above devices. To further improve these interactions, there is a growing interest in research community for displaying active tactile feedback to users through the capacitive screens. One approach followed for this purpose is to control the friction force between finger pad of user and the screen via electrostatic actuation. If an alternating voltage is applied to the conductive layer of a touch screen, an attraction force is generated between the finger and its surface. This force modulates the friction between the surface and the skin of the finger moving on it. Hence, one can generate different haptic effects on a touch screen by controlling the amplitude, frequency and waveform of this input voltage. These haptic effects could be used to develop new intelligent user interfaces, in applications involving education, data visualization, and digital games. However, this area of research is new and we do not fully understand the electro-mechanical interactions between human finger and the touch screen actuated by electrostatic forces and the effect of these interactions on our haptic perception yet. Hence, the aim of this project is to investigate the electromechanical interactions between human finger and an electrostatically actuated touch screen in depth. In particular, we will investigate the effect of following factors on the frictional forces between finger and the screen; a) amplitude of the voltage applied to the conductive layer of the touch screen, b) the normal force applied by finger on the touch screen, and c) effect of finger speed. The results of this study will not only enable us to better understand the physics of interactions between human finger and a touch screen actuated by electrostatic forces from a scientific point of view, but will also provide us with guidelines on how to program a touch screen to generate desired haptic effects for various applications.

Transfer Learning of Robotic Skills from Naïve User Demonstrations

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 2018-2021
Principal Investigator: B. Akgün

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Robots and related component technologies are getting more capable, affordable and accessible. With the advent of safe collaborative robotic arms, the number of “cage-free” robots are increasing. However, as they become more ubiquitous, the range of tasks and environments they face grow more complex. Many of these environments, such as households, machine-shops, hospitals, and schools, contain people having a wide range of preferences, expectations, assumptions, and level of technological savviness. Future robot users will want to customize their robot behavior and add new ones. Thus it is not practical to program robots for all the scenarios that they will
face when they are deployed. The field of Learning from Demonstration (LfD) emerged as answer to this challenge, with the vision of programming robots through demonstrations of the desired behavior instead of explicit
programming. Most existing LfD approaches learn a new skill from scratch, but there will inevitably be many skills required from the robot. After a certain point, teaching each skill like this would get tedious. Instead, the robot should transfer knowledge from its already learned skills. The aim of this project is to learn robotic skills from non-robotics experts and use previously learned skills to either speed up learning or increase generalization. Towards this end, this project investigates three topics; (1) design a joint action-goal model to facilitate transfer learning, (2) feature learning for skill transfer and (3) improve existing interactions for LfD or develop new ones for transfer learning.

Adaptive Fractional Order Controller Design Using Machine Learning for Physical Human-Robot Interaction

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 2018-2021
Principal investigator: Ç. Başdoğan

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In the near future, humans and robots are expected to perform collaborative tasks involving physical interaction in various different environments such as homes, hospitals, and factories. One important research topic in physical Human-Robot Interaction (pHRI) is to develop natural haptic communication between the partners. Although there is already a large body of studies in the area of human-robot Interaction, the number of studies investigating the physical interaction between the partners and in particular the haptic communication are limited and the interaction in such systems is still artificial when compared to natural human-human collaboration. Although the collaborative tasks involving physical interaction such as assembly/disassembly of parts and transportation of an object can be planned and executed naturally and intuitively by two humans, there are unfortunately no robots in the market that can collaborate and perform the same tasks with us. In this project, we propose a fractional order adaptive control for the pHRI systems. The main goal of the project is to adapt the admittance parameters of the robot in real-time during the task, based on the changes in human and environment impedances, while balancing the tradeoff between the stability and the transparency of the coupled system. To the best of our knowledge, there is no earlier study in the literature utilizing a fractional order admittance controller for pHRI. Compared to an integer order controller, a fractional order controller enables to use fractional order derivative and integrator, which will bring us flexibility in modeling and controlling the dynamics of physical interactions between the human operator and the robot. Moreover, there is no study in literature investigating the real-time adaptation of the control parameters of a fractional order admittance controller via machine learning algorithms. Machine learning algorithms will enable us to learn from data iteratively to estimate human intention during the task and then select control parameters accordingly to optimize the task performance.

Image and Video Processing by Deep Learning

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 2018-2021
Principal Investigator: M. A. Tekalp

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The advent of deep learning is changing how we do 2D/3D image/video processing, including image/video restoration, interpolation, super-resolution, motion analysis/tracking, and compression, and light-field and hologram processing. Various deep neural network (DNN) architectures, such as convolutional neural networks (CNN), auto-encoders, recurrent neural networks (RNN), generative adversarial networks (GAN) have already been applied to different image/video processing problems. The question then arises whether data-driven deep networks and associated learning algorithms have become the preferred dominant solution to all image/video processing problems, in contrast to the traditional human-engineered, hand-crafted algorithms using domain-specific signals-systems models. The answer to this question is almost surely affirmative and deep image/video processing methods are poised to replace a large part of traditional image/video processing pipeline.

Yet, deep signal processing is a very young field, the science of DNN and how they produce such amazing image/video processing results are not sufficiently well understood and more research is needed for a clear theoretical understanding of which DNN architectures work best for what image/video processing problems and how can we obtain much better and more stable results. The current successes of deep learning in image/video processing are experimentally-driven by more-or-less on trial and error. There are several open challenges, e.g., IMAGENet large scale visual recognition, visual object tracking (VOT), large scale activity recognition (ActivityNet), and single-image super-resolution (NTIRE), and a different network architecture wins the competition in different challenges each year. Few formal works exist to understand the mathematics behind this.

This project will explore the potential for breakthrough in image and video processing using new deep learning algorithms, guided by machine-learned signal models. We believe that the relatively less studied areas of residual learning, adversarial learning, and reinforcement learning offer high-potential for image and video processing. This project will investigate some fundamental questions within a formal framework and explore the potential for further breakthrough in image/video processing, including problems that have not been addressed by using DNN, such as motion-compensated video processing, video compression, and light-field and hologram processing/compression, using deep learning guided by big-data-driven learned signal models. The proposed research is groundbreaking because it brings in new ideas, which can revolutionize the way we do image/video processing rendering some of the traditional algorithms obsolete.

Summarization Approaches Towards Interpreting Big Visual Data

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 2017-2020
Principal Investigator: A. Erdem

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Backchannel Feedback Modelling for Human-Computer Interaction

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 20018-2020
Principal Investigator: E. Erzin

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Data Locality Abstractions for Unstructured Meshes in High-Performance Reservoir Simulation

Funded by: Saudi Aramco
Dates: 20017-2020
Principal Investigator: D. Unat

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The projects listed below have been completed at Koc University before the KUIS AI

JOKER: Joke and Empathy of a Robot/ECA: Towards Social and Affective Relations with a Robot

Funded by: European Commission ERA-Net Program, CHIST-ERA Intelligent User Interfaces Call
Dates: 2013-2016
Principal investigator: T. M. Sezgin

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This project will build and develop JOKER, a generic intelligent user interface providing a multimodal dialogue system with social communication skills including humor, empathy, compassion, charm, and other informal socially-oriented behavior.

Talk during social interactions naturally involves the exchange of propositional content but also and perhaps more importantly the expression of interpersonal relationships, as well as displays of emotion, affect, interest, etc. This project will facilitate advanced dialogues employing complex social behaviors in order to provide a companion-machine (robot or ECA) with the skills to create and maintain a long term social relationship through verbal and non verbal language interaction. Such social interaction requires that the robot has the ability to represent and understand some complex human social behavior. It is not straightforward to design a robot with such abilities. Social interactions require social intelligence and ‘understanding’ (for planning ahead and dealing with new circumstances) and employ theory of mind for inferring the cognitive states of another person.

JOKER will emphasize the fusion of verbal and non-verbal channels for emotional and social behavior perception, interaction and generation capabilities. Our paradigm invokes two types of decision: intuitive (mainly based upon non-verbal multimodal cues) and cognitive (based upon fusion of semantic and contextual information with non-verbal multimodal cues.) The intuitive type will be used dynamically in the interaction at the non-verbal level (empathic behavior: synchrony of mimics such as smile, nods) but also at verbal levels for reflex small- talk (politeness behavior: verbal synchrony with hello, how are you, thanks, etc). Cognitive decisions will be used for reasoning on the strategy of the dialog and deciding more complex social behaviors (humor, compassion, white lies, etc.) taking into account the user profile and contextual information.

JOKER will react in real-time with a robust perception module (sensing user’s facial expressions, gaze, voice, audio and speech style and content), a social interaction module modelling user and context, with long-term memories, and a generation and synthesis module for maintaining social engagement with the user.

The research will provide a generic intelligent user interface for use with various platforms such as robots or ECAs, a collection of multimodal data with different socially-oriented behavior scenarios in two languages (French and English) and an evaluation protocol for such systems. Using the database collected in a human-machine context, cultural aspects of emotions and natural social interaction including chat, jokes, and other informal socially-oriented behavior will be incorporated.

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IMOTION : Intelligent Multimodal Augmented Video Motion Retrieval System

This work was partly supported by the Chist-Era project IMOTION with contributions from the Belgian Fonds de la Recherche Scientifique (FNRS, contract no. R.50.02.14.F), the Scientific and Technological Research Council of Türkiye (T¨ubitak, grant no. 113E325), and the Swiss National Science Foundation (SNSF, contract no. 20CH21 151571).
Dates: 2013-2016
Principal investigator: T. M. Sezgin

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The IMOTION project develops and evaluates innovative multi-modal user interfaces for interacting with augmented videos. Starting with an extension of existing query paradigms (keyword search in manual annotations), image search (query by example in key frames), IMOTION considers novel sketch- and speech-based user interfaces.

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Intelligent Interfaces for eLearning

Funded by: Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Dates: 2013-2016
Principal investigator: T. M. Sezgin

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The goal of this project is to build the pen-based interfaces for the classroom of the future. Currently there is little interaction and personalized feedback between instructors and pupils. We use realtime processing of pen input to create consolidated representations of student interactions and allow teachers to give timely and to-the-point feedback to students to enhance the learning experience.

Semi-supervised Intelligent Multimodal Content Translator for Smart TVs

Funded by: SANTEZ Programme, Ministry of Science, Industry, and Technology, Türkiye
Dates: 2012-2015
Principal investigator: T. M. Sezgin

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TVs are slowly morphing into powerful set-top computers with internet connections. As such, they slowly take over roles and functions that were traditionally associated with desktop computers. TV users, for example, can use their TV for browsing the internet. Unfortunately, the vast majority of the content in the internet has been designed for desktop viewing, hence they have to be adapted for viewing on a TV. In this Project, we aim to develop a semi-automatic content retargeting system, which is expected to work with minimal intervention of an expert.

Internet-Based Environment for Social Inclusion of Children w/Autism Spectrum Conditions

Funded by: European Community’s Seventh Framework Programm
Dates: 2011-2014
Principal investigator: T. M. Sezgin

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The main goal of this project is to develop a computer software program that will assist children with Autism Spectrum Conditions (ASC) to understand and express emotions through facial expressions, tone-of-voice and body gestures.This software will assist them to understand and interact with other people, and as a result, will increase their inclusion in society.

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Deep Green: Commander’s Associate (sketch-to-plan module)

Funded by: DARPA/BAE/SIFT (British Aerospace/Smart Information Flow Technologies)
Dates: 2008-2009
Principal investigator: T. M. Sezgin (co-PI for the sketch-to-plan module)

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Deep Green is a project that ran under the Information Processing Technology Office of the Defense Advanced Research Projects Agency. The purpose of the project was to develop a decision-making support system for United States Army commanders. The systems developed feature advanced predictive capabilities to enable computers to efficiently and accurately predict possible future scenarios, based on an analysis of the current situation, in order to give army commanders a better view of possible outcomes for their decisions [1][2][3] Deep Green is composed of four major components: Blitzkrieg – Battlefield model which analyzes current situation and determines possible future outcomes for use in planning. When a plan is presented, Blitzkrieg analyzes the plan to point out possible results of that course of action to the commander. Blitzkrieg itself does not do planning, it merely determines the likely results of a plan formulated by a human commander. Crystal Ball – Performs analysis of possible futures generated from blitzkrieg, and determines the “best” choices by measuring flexibility, usefulness, and likelihood of each. It picks the best of these choices and presents them to the commander. Also updates model of battlefield situation with information pulled from the field. This might include reports from soldiers, through a program similar to the Communicator program that was developed under the Information Awareness Office or through automated RSTA systems such as HART. Commander’s Associate – this is the user interface and visualization component. It consists of “Sketch-to-decide” which presents the commander with a list of options, and “Sketch-to-plan” which is a screen on which the commander can draw up a plan, which Deep Green will interpret and put into action

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