Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10679/43

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Now showing 1 - 20 of 542

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An architecture viewpoint for modeling dynamically configurable software systems
(Elsevier, 2017-01-01) Tekinerdogan, B.; Sözer, Hasan; Computer Science; SÖZER, Hasan
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 architecture it is important to model the adaptability concerns explicitly. In practice, architectural concerns are represented using architecture views that are derived from the corresponding architecture viewpoints. Different software architecture viewpoints have been introduced but runtime adaptability has not been broadly considered and remains implicit in the architecture views. In this paper we introduce the adaptability viewpoint that can be used for modeling dynamically configurable software architectures. We illustrate the viewpoint for a demand-driven supply chain management system.
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Networking standards
(IEEE, 2017-03) Beğen, Ali Cengiz; Bök, P. B.; Saltsidis, P.; Computer Science; BEĞEN, Ali Cengiz
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, more reliable, ubiquitous services with an ever-increasing scale over the communications infrastructure. This is causing a shift from traditional standards development to a hybrid approach that includes open-source development techniques, tooling, and full lifecycle management. Keeping pace with the changes to the standards ecosystem and evolution of the way networks are built and deployed is challenging. Additionally, the Internet of Things (IoT) is one of the main drivers which, on the one hand, increases the number of connected communicative components, and on the other hand pushes the development of a huge amount of standards.
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Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks
(IEEE, 2017-01-01) Narmanlıoğlu, Ö.; Zeydan, E.; Kandemir, Melih; Kranda, T.; Computer Science; KANDEMİR, Malih
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) Advanced have increasingly gaining momentum in order to handle with data explosion. On the other hand, the deployment of new network equipment is resulting in increasing both capital and operating expenditures. These deployments are done under the consideration of the busy hour periods which the network experiences the highest amount of traffic. However, these periods refer to only a couple of hours over a 24-hour period. In relation to this, accurate prediction of active user equipment (UE) number is significant for efficient network operations and results in decreasing energy consumption. In this paper, we investigate a Bayesian technique to design an optimal feed-forward neural network for shortterm predictor executed at the network management entity and providing proactivity to Energy Saving, a Self-Organizing Network function. We first demonstrate prediction results of active UE number collected from real LTE network. Then, we evaluate the prediction accuracy of the Bayesian neural network as comparing with low complex naive prediction method, Holt- Winter's exponential smoothing method, a deterministic feedforward neural network without Bayesian regularization term.
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Networking standards
(IEEE, 2017-12) Beğen, Ali Cengiz; Bok, P. B.; Saltsidis, P.; Computer Science; BEĞEN, Ali Cengiz
N/A
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From capturing to rendering: Volumetric media delivery with six degrees of freedom
(IEEE, 2020-10) Van Der Hooft, J.; Vega, M. T.; Wauters, T.; Timmerer, C.; Beğen, Ali Cengiz; De Turck, F.; Schatz, R.; Computer Science; BEĞEN, Ali Cengiz
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 applications such as remote surgery and virtual reality. Recent research made six degrees of freedom (6DoF) for immersive media possible, where users may both move their head and change their position within a scene. In this article, we present the status and challenges of 6DoF applications based on volumetric media, focusing on the key aspects required to deliver such services. Furthermore, we present results from a subjective study to highlight relevant directions for future research.
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OzU-NLP at TREC NEWS 2019: Entity ranking
(National Institute of Standards and Technology (NIST), 2019) Fayoumi, Kenan; Yeniterzi, R.; Fayoumi, Kenan
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 wiki-pages of the entities, Doc2Vec provides us with vector representations for these two that we can utilize to perform ranking on entities. We also investigate whether background linked articles can be useful for entity ranking task.
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Sampling-free variational inference of bayesian neural networks by variance backpropagation
(ML Research Press, 2020) Haußmann, M.; Hamprecht, F. A.; Kandemir, Melih; Computer Science; KANDEMİR, Malih
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 ReLU nonlinearities into the product of an identity and a Heaviside step function, (ii) introducing a separate path that decomposes the neural net expectation from its variance. We demonstrate formally that introducing separate latent binary variables to the activations allows representing the neural network likelihood as a chain of linear operations. Performing variational inference on this construction enables a sampling-free computation of the evidence lower bound which is a more effective approximation than the widely applied Monte Carlo sampling and CLT related techniques. We evaluate the model on a range of regression and classification tasks against BNN inference alternatives, showing competitive or improved performance over the current state-of-the-art.
Open Access
ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
(ML Research Press, 2020) Akbulut, M. T.; Öztop, Erhan; Xue, H.; Tekden, A. E.; Şeker, M. Y.; Uğur, E.; Computer Science; ÖZTOP, Erhan
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). In this paper, we propose a novel LfD+RL framework, namely Adaptive Conditional Neural Movement Primitives (ACNMP), that allows efficient policy improvement in novel environments and effective skill transfer between different agents. This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous training of the model with supervised learning (SL) for acquiring the demonstrated trajectories and via RL for new trajectory discovery. Through simulation experiments, we show that (i) ACNMP enables the system to extrapolate to situations where pure LfD fails; (ii) Simultaneous training of the system through SL and RL preserves the shape of demonstrations while adapting to novel situations due to the shared representations used by both learners; (iii) ACNMP enables order-of-magnitude sample-efficient RL in extrapolation of reaching tasks compared to the existing approaches; (iv) ACNMPs can be used to implement skill transfer between robots having different morphology, with competitive learning speeds and importantly with less number of assumptions compared to the state-of-the-art approaches. Finally, we show the real-world suitability of ACNMPs through real robot experiments that involve obstacle avoidance, pick and place and pouring actions.
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Uncertainty-aware deep classifiers using generative models
(Association for the Advancement of Artificial Intelligence, 2020) Şensoy, Murat; Kaplan, L.; Cerutti, F.; Saleki, Maryam; Computer Science; ŞENSOY, Murat; Saleki, Maryam
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 output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.
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A benchmark for inpainting of clothing images with irregular holes
(Springer, 2020) Kınlı, Osman Furkan; Özcan, Barış; Kıraç, Mustafa Furkan; Computer Science; KINLI, Osman Furkan; KIRAÇ, Mustafa Furkan; Özcan, Barış
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 not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step, and empirically show that the proposed method reduces the required number of layers to form fully-transparent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image.
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Exploring scaling efficiency of intel loihi neuromorphic processor
(IEEE, 2023) Uludağ, Recep Buğra; Çaǧdaş, S.; Işler, Y. S.; Şengör, N. S.; Aktürk, İsmail; Computer Science; AKTÜRK, Ismail; Uludağ, Recep Buğra
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 scheme change. By analyzing these relationships, our study aims to shed light on the intricate interplay between SNN features and the efficiency of neuromorphic systems as they scale up. The findings presented in this paper are expected to enhance the comprehension of scaling efficiency in neuromorphic hardware, providing valuable insights for researchers and developers in optimizing the performance of large-scale SNNs on neuromorphic architectures.
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Effects of agent's embodiment in human-agent negotiations
(ACM, 2023-09-19) Çakan, Umut; Keskin, Mehmet Onur; Aydoǧan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Çakan, Umut; Keskin, Mehmet Onur
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 protocol (i.e., what to exchange and how) and strategy (i.e., how to generate offers and when to accept). Apart from these components, the embodiment may implicitly influence the negotiation process and outcome. The perception of a physically embodied agent might differ from the virtually embodied one; thus, it might influence human negotiators’ decisions and responses. Accordingly, this work empirically studies the effect of physical and virtual embodiment in human-agent negotiations. We designed and conducted experiments where human participants negotiate with a humanoid robot in one setting, whereas they negotiate with a virtually embodied replica of that robot in another setting. The experimental results showed that social welfare was statistically significantly higher when the negotiation was held with a virtually embodied robot rather than a physical robot. Human participants took the negotiation more seriously against physically embodied agents and made more collaborative moves in the virtual setting. Furthermore, their survey responses indicate that participants perceived our robot as more humanlike when it is physically embodied.
Open Access
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
(Springer, 2024-02-12) Barakat, Huda Mohammed Mohammed; Turk, O.; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Barakat, Huda Mohammed Mohammed
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 quality, closely mimicking human speech. Nevertheless, given the wide array of applications now employing TTS models, mere high-quality speech generation is no longer sufficient. Present-day TTS models must also excel at producing expressive speech that can convey various speaking styles and emotions, akin to human speech. Consequently, researchers have concentrated their efforts on developing more efficient models for expressive speech synthesis in recent years. This paper presents a systematic review of the literature on expressive speech synthesis models published within the last 5 years, with a particular emphasis on approaches based on deep learning. We offer a comprehensive classification scheme for these models and provide concise descriptions of models falling into each category. Additionally, we summarize the principal challenges encountered in this research domain and outline the strategies employed to tackle these challenges as documented in the literature. In the Section 8, we pinpoint some research gaps in this field that necessitate further exploration. Our objective with this work is to give an all-encompassing overview of this hot research area to offer guidance to interested researchers and future endeavors in this field.
Open Access
Towards interactive explanation-based nutrition virtual coaching systems
(Springer, 2024-01) Buzcu, Berk; Tessa, M.; Tchappi, I.; Najjar, A.; Hulstijn, J.; Calvaresi, D.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Buzcu, Berk
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 virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.
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Discovering predictive relational object symbols with symbolic attentive layers
(IEEE, 2024-02-01) Ahmetoglu, A.; Celik, B.; Öztop, Erhan; Uğur, E.; Computer Science; ÖZTOP, Erhan
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 with multiple objects in a tabletop environment. The key feature of the model is that it can take a changing number of objects as input and map the object-object relations into symbolic domain explicitly. In the model, we employ a self-attention layer that computes discrete attention weights from object features, which are treated as relational symbols between objects. These relational symbols are then used to aggregate the learned object symbols and predict the effects of executed actions on each object. The result is a pipeline that allows the formation of object symbols and relational symbols from a dataset of object features, actions, and effects in an end-to-end manner. We compare the performance of our proposed architecture with state-of-the-art symbol discovery methods in a simulated tabletop environment where the robot needs to discover symbols related to the relative positions of objects to predict the action's result. Our experiments show that the proposed architecture performs better than other baselines in effect prediction while forming not only object symbols but also relational symbols.
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Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework
(IEEE, 2023) Taliaronak, V.; Lange, A. L.; Kırtay, M.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan
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 robot trust model relies on assessing the trustworthiness of the interaction partners based on the computational cognitive load incurred during the interactive task [1]. However, this model does not take into account the social markers indicative of trustworthiness, such as the gestures displayed by a human partner. In this study, we make a step toward this point by extending the model by integrating a social cue processing module to achieve social human-robot interaction. This new model serves as a novel social cognitive trust framework to enable the Pepper robot to evaluate the trustworthiness of its interaction partners based on both cognitive load (i.e., the cost of perceptual processing) and social cues (i.e., their gestures). For evaluating the efficacy of the framework, the Pepper robot with the developed model is put to interact with human partners who may take the roles of a reliable, unreliable, deceptive, or random suggestion providing partner. Overall, the results indicate that the proposed framework allows the Pepper robot to differentiate the guiding strategies of the partners by detecting deceptive partners and thus select a trustworthy partner in case of a free choice to perform the next task.
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EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things
(Elsevier, 2024-01) Kashif, Muhammad; Çakmakçı, Kübra Kalkan; Computer Science; ÇAKMAKCİ, Kübra Kalkan; Kashif, Muhammad
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 significant attention nowadays. These IoT devices may contain highly sensitive data and data sharing processes which may lead to security and privacy concerns. To surmount these issues, the interaction of IoT with blockchain for a secure transaction is accepted as a candidate solution. However, the innate behavior of blockchain containing complex mathematical proofs and consensus protocol requires high computational power making it less favorable for IoT devices to be connected with. Motivated by a private by-design framework and emphasizing greater control and setting of privacy preferences by the data owner, this paper complements our previous work on privacy preservation in IoT networks. In this paper, we design and propound a complete blockchain-based privacy-preserving framework by deploying service-oriented layers concepts and low computation cryptography, and a less complex consensus protocol to address the privacy concern. Moreover, this paper will unravel the complete end-to-end architecture of IoT-based blockchain purposely build for secure transactions in IoT networks. Security analysis is conducted using AVISPA tool to show that the proposed algorithms attain the desired security goals. This is followed by extensive simulation experiments and ultimate output results cultivating it much favorably for the deployment of IoT applications in real life.
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Feature extraction for enhancing data-driven urban building energy models
(European Council on Computing in Construction (EC3), 2023) Bolluk, Muhammed Said; Seyis, Senem; Aydoğan, Reyhan; Computer Science; Civil Engineering; KAZAZOĞLU, Senem Seyis; AYDOĞAN, Reyhan; Bolluk, Muhammed Said
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 in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model that estimates the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score. Therefore, this study shows that building energy consumption estimation can be enhanced via new feature extraction equipped with domain knowledge.
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Towards test automation for certification tests in the banking domain
(IEEE, 2023) Elakas, A.; Tarlan, Ozan; Safak, I.; Çakmakçı, Kübra Kalkan; Sözer, Hasan; Computer Science; SÖZER, Hasan; ÇAKMAKCİ, Kübra Kalkan; Tarlan, Ozan
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. In this paper, we suggest that automation of the certificate tests are possible and it will save a considerable amount of time. A certification testing operation which can take a few weeks can be reduced to a few seconds. Firstly, we review the existing test activities to identify the ones that can be automated and introduce a prototype tool for automating some of the tests used for certification. We focus on rules that are verified by analyzing the banking infrastructure. Our tool takes the network topology of the banking infrastructure as input and verifies a subset of these rules. The tool can be extended with additional rules in order to reduce the effort for certification tests. In addition to this tool, we introduce softwaredefined network-based tests to automatically verify compliance with the rules by checking the firewall constraints and host connections. In particular, we focus on a security certification standard named Payment Card Industry Data Security Standard. This certification aims to reduce the risk of data breaches in cardholder data by ensuring industry standard practices for payment card transactions. Our tool offers effort reduction in auditing through automation. It supports continuous auditing and network security enhancement processes.
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Point of sale Fraud detection methods via machine learning
(IEEE, 2023) Begen, E.; Sayan, İ. U.; Bayrak, A. T.; Yıldız, Olcay Taner; Computer Science; YILDIZ, Olcay Taner
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 remains an ongoing concern. In this study, machine learning methods are used to detect fraudulent transactions at the cash register in fast-food restaurants. By using POS logs, transactions in restaurants are recorded and these logs are analyzed to detect fraudulent transactions on an unbalanced dataset. Random forest, XGBoost and LGBM algorithms are used in the study and different resampling techniques (ADASYN etc.) are applied to improve the performance of these algorithms. In addition, it is aimed to find the best parameters with the randomized search method. In conclusion, this study offers a solution for detecting fraudulent transactions at the cash register in fast-food restaurants. The results of the study are promising in its current state.

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