Faculty of Engineering

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Open Access
CL-FedFR: Curriculum learning for federated face recognition
(SciTePress , 2024-02-29) Dube, D. C.; Eroğlu Erdem, Ciğdem ; Korcak, Ö.; Electrical & Electronics Engineering; ERDEM, Çiğdem Eroğlu
Face recognition (FR) has been significantly enhanced by the advent and continuous improvement of deep learning algorithms and accessibility of large datasets. However, privacy concerns raised by using and distributing face image datasets have emerged as a significant barrier to the deployment of centralized machine learning algorithms. Recently, federated learning (FL) has gained popularity since the private data at edge devices (clients) does not need to be shared to train a model. FL also continues to drive FR research toward decentralization. In this paper, we propose novel data-based and client-based curriculum learning (CL) approaches for federated FR intending to improve the performance of generic and client-specific personalized models. The data-based curriculum utilizes head pose angles as the difficulty measure and feeds the images from “easy” to “difficult” during training, which resembles the way humans learn. Client-based curriculum chooses “easy clients” based on performance during the initial rounds of training and includes more “difficult clients” at later rounds. To the best of our knowledge, this is the first paper to explore CL for FR in a FL setting. We evaluate the proposed algorithm on MS-Celeb-1M and IJB-C datasets and the results show an improved performance when CL is utilized during training.
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Extraction of novel features based on histograms of MFCCs used in emotion classification from generated original speech dataset
(2020) Pakyurek, M.; Atmış, Mahir; Kulac, S.; Uludag, U.; Atmış, Mahir
This paper introduces two significant contributions: one is a new feature based on histograms of MFCC (Mel-Frequency Cepstral Coefficients) extracted from the audio files that can be used in emotion classification from speech signals, and the other – our new multi-lingual and multi-personal speech database, which has three emotions. In this study, Berlin Database (BD) (in German) and our custom PAU database (in English) created from YouTube videos and popular TV shows are employed to train and evaluate the test results. Experimental results show that our proposed features lead to better classification of results than the current state-of-the-art approaches with Support Vector Machine (SVM) from the literature. Thanks to our novel feature, this study can outperform a number of MFCC features and SVM classifier based studies, including recent researches. Due to the lack of our novel feature based approaches, one of the most common MFCC and SVM framework is implemented and one of the most common database Berlin DB is used to compare our novel approach with these kind of approaches.
Open Access
Force reference extraction via human interaction for a robotic polishing task: Force-induced motion
(IEEE, 2019-10) Hamdan, Sara; Öztop, Erhan; Uğurlu, Regaip Barkan; Computer Science; Mechanical Engineering; ÖZTOP, Erhan; UĞURLU, Regaip Barkan; Hamdan, Sara
In this paper, a method to control a manipulator using force-induced trajectory is proposed. The trajectory is learned from an operator doing the polishing task using a tool attached to the robot's end-effector. The learning process is performed by a deep neural network which is designed and trained to generate a force profile according to the states (joints' positions and velocities). The admittance control technique is utilized to make the manipulator compliant to the operator movements in the teaching mode. Spring-Damper system along with Inertia-Damper system has been studied to impose the relationship between the operator's applied force and the reaction of the manipulator. The universal robot (UR5) aside with a force sensor (OptoForce) are used to run the experiment. Robot Operation System (ROS) is used to accomplish the task in real-time. The polishing task is learned and achieved by the robot itself, and the force trajectories are better followed using the Inertia-Damper system as the admittance controlling scheme.
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High-level features for resource economy and fast learning in skill transfer
(Taylor & Francis, 2022) Ahmetoglu, A.; Uğur, E.; Asada, M.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan
Abstraction is an important aspect of intelligence which enables agents to construct robust representations for effective and efficient decision making. Although, deep neural networks are proven to be effective learning systems due to their ability to form increasingly complex abstractions at successive layers these abstractions are mostly distributed over many neurons, making the re-use of a learned skill costly and blind to the insights that can be obtained on the emergent representations. For avoiding designer bias and unsparing resource use, we propose to exploit neural response dynamics to form compact representations to use in skill transfer. For this, we consider two competing methods based on (1) maximum information compression principle and (2) the notion that abstract events tend to generate slowly changing signals, and apply them to the neural signals generated during task execution. To be concrete, in our simulation experiments, we either apply principal component analysis (PCA) or slow feature analysis (SFA) on the signals collected from the last hidden layer of a deep neural network while it performs a source task, and use these features for skill transfer in a new, target, task. We then compare the generalization and learning performance of these alternatives with the baselines of skill transfer with full layer output and no-transfer settings. Our experimental results on a simulated tabletop robot arm navigation task show that units that are created with SFA are the most successful for skill transfer. SFA as well as PCA, incur less resources compared to usual skill transfer where full layer outputs are used in the new task learning, whereby many units formed show a localized response reflecting end-effector-obstacle-goal relations. Finally, SFA units with the lowest eigenvalues resemble symbolic representations that highly correlate with high-level features such as joint angles and end-effector position which might be thought of as precursors for fully symbolic systems.
Open Access
SCARA with Path trajectory
(2008) Elamassie, Mohammed; Elamassie, Mohammed
The following Matlab project contains the source code and Matlab examples used for SCARA with Path trajectory. By defining the initial position and final position the robot will follow the path between these two points

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