Computer Science

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

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Open Access
Actor-critic reinforcement learning for bidding in bilateral negotiation
(TÜBİTAK, 2022) Arslan, Furkan; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Arslan, Furkan
Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of the coming offer based on previous offer exchanges and remaining time. Furthermore, an imitation learning approach called behavior cloning is adopted to speed up the learning process. Also, a novel reward function is introduced that does take not only the agent’s own utility but also the opponent’s utility at the end of the negotiation. The developed agent is empirically evaluated. Thus, a large number of negotiation sessions are run against a variety of opponents selected in different domains varying in size and opposition. The agent’s performance is compared with its opponents and the performance of the baseline agents negotiating with the same opponents. The empirical results show that our agent successfully negotiates against challenging opponents in different negotiation scenarios without requiring any former information about the opponent or domain in advance. Furthermore, it achieves better results than the baseline agents regarding the received utility at the end of the successful negotiations.
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Adaptive shared control with human intention estimation for human agent collaboration
(IEEE, 2022) Amirshirzad, Negin; Uğur, E.; Bebek, Özkan; Öztop, Erhan; Computer Science; Mechanical Engineering; BEBEK, Özkan; ÖZTOP, Erhan; Amirshirzad, Negin
In this paper an adaptive shared control frame-work for human agent collaboration is introduced. In this framework the agent predicts the human intention with a confidence factor that also serves as the control blending parameter, that is used to combine the human and agent control commands to drive a robot or a manipulator. While performing a given task, the blending parameter is dynamically updated as the result of the interplay between human and agent control. In a scenario where additional trajectories need to be taught to the agent, either new human demonstrations can be generated and given to the learning system, or alternatively the aforementioned shared control system can be used to generate new demonstrations. The simulation study conducted in this study shows that the latter approach is more beneficial. The latter approach creates improved collaboration between the human and the agent, by decreasing the human effort and increasing the compatibility of the human and agent control commands.
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Anti-spoofing for text-independent speaker verification: An initial database, comparison of countermeasures, and human performance
(IEEE, 2016-04) Wu, Z.; Leon, P. L. de; Demiroğlu, Cenk; Khodabakhsh, Ali; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Khodabakhsh, Ali
In this paper, we present a systematic study of the vulnerability of automatic speaker verification to a diverse range of spoofing attacks. We start with a thorough analysis of the spoofing effects of five speech synthesis and eight voice conversion systems, and the vulnerability of three speaker verification systems under those attacks. We then introduce a number of countermeasures to prevent spoofing attacks from both known and unknown attackers. Known attackers are spoofing systems whose output was used to train the countermeasures, while an unknown attacker is a spoofing system whose output was not available to the countermeasures during training. Finally, we benchmark automatic systems against human performance on both speaker verification and spoofing detection tasks.
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Autotuning runtime specialization for sparse matrix-vector multiplication
(ACM, 2016-04) Yılmaz, Buse; Aktemur, Tankut Barış; Garzaran, M. J.; Kamin, S.; Kıraç, Mustafa Furkan; Computer Science; AKTEMUR, Tankut Bariş; KIRAÇ, Mustafa Furkan; Yılmaz, Buse
Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.
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A big data processing framework for self-healing internet of things applications
(IEEE, 2016) Dundar, B.; Astekin, Merve; Aktas, M. S.; Astekin, Merve
In this study, we introduce a big data processing framework that provides self-healing capability in the Internet of Things domain. We discuss the high-level architecture of this framework and its prototype implementation. To identify faulty conditions, we utilize a complex-event processing technique by applying a rule-based pattern-detection algorithm on the events generated real-time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed architecture, we test the prototype implementation for performance and scalability under increasing incoming message rates. The results are promising, because its processing overhead is negligible.
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Büyük veri problemlerine çözüm olarak veri akış madenciliği
(IEEE, 2013) Ölmezoğulları, Erdi; Arı, İsmail; Çelebi, Ö. F.; Ergüt, S.; Computer Science; ARI, Ismail; Ölmezoğulları, Erdi
Günümüzde bilişim dünyası faydalı bilgiye ulaşma yolunda “büyük veri” problemleri (verinin kütlesi, hızı, çeşitliliği, tutarsızlığı) ile baş etmeye çalışmaktadır. Bu makalede, büyük veri akışları üzerinde İlişkisel Kural Madenciliği’nin (İKM) daha önce literatürde yapılmamış bir şekilde “çevrimiçi” olarak gerçeklenme detayları ile başarım bulguları paylaşılacaktır. Akış madenciliği için Apriori ile FP-Growth algoritmaları Esper isimli olay akış motoruna eklenmiştir. Elde edilen sistem üzerinde bu iki algoritma kayan penceler ve LastFM sosyal müzik sitesi verileri kullanılarak karşılaştırılmıştır. Başarımı yüksek olan FPGrowth seçilerek gerçek-zamanlı ve kural-tabanlı bir tavsiye motoru oluşturulması sağlanmıştır. En önemli bulgularımız çevrimiçi kural çıkarımı sayesinde: (1) çevrimdışı kural çıkarımından çok daha fazla kuralın (2) çok daha hızlı ve etkin olarak ve (3) çok daha önceden hesaplanabileceği gösterilmiştir. Ayrıca müzik zevklerine uygun “George Harrison⇒The Beatles” gibi pekçok ilginç ve gerçekçi kural bulunmuştur. Sonuçlarımızın ileride diğer büyük veri analitik sistemlerinin tasarım ve gerçeklemesine ışık tutacağını ummaktayız.
Open Access
Can social agents efficiently perform in automated negotiation?
(MDPI, 2021-07) Sanchez-Anguix, V.; Tunalı, O.; Aydoğan, Reyhan; Julian, V.; Computer Science; AYDOĞAN, Reyhan
In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.
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Cognition-enabled robot manipulation in human environments: requirements, recent work, and open problems
(IEEE, 2017-09) Ersen, M.; Öztop, Erhan; Sariel, S.; Computer Science; ÖZTOP, Erhan
Service robots are expected to play an important role in our daily lives as our companions in home and work environments in the near future. An important requirement for fulfilling this expectation is to equip robots with skills to perform everyday manipulation tasks, the success of which is crucial for most home chores, such as cooking, cleaning, and shopping. Robots have been used successfully for manipulation tasks in wellstructured and controlled factory environments for decades. Designing skills for robots working in uncontrolled human environments raises many potential challenges in various subdisciplines, such as computer vision, automated planning, and human-robot interaction. In spite of the recent progress in these fields, there are still challenges to tackle. This article outlines problems in different research areas related to mobile manipulation from the cognitive perspective, reviews recently published works and the state-of-the-art approaches to address these problems, and discusses open problems to be solved to realize robot assistants that can be used in manipulation tasks in unstructured human environments.
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Combining haar feature and skin color based classifiers for face detection
(IEEE, 2011) Eroğlu Erdem, Ç.; Ulukaya, S.; Karaali, A.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
This paper presents a hybrid method for face detection in color images. The well known Haar feature-based face detector developed by Viola and Jones (VJ), that has been designed for gray-scale images is combined with a skin-color filter, which provides complementary information in color images. The image is first passed through a Haar-Feature based face detector, which is adjusted such that it is operating at a point on its ROC curve that has a low number of missed faces but a high number of false detections. Then, using the proposed skin color post-filtering method many of these false detections can be eliminated easily. We also use a color compensation algorithm to reduce the effects of lighting. Our experimental results on the Bao color face database show that the proposed method is superior to the original VJ algorithm and also to other skin color based pre-filtering methods in the literature in terms of precision.
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Combining semantic web and IoT to reason with health and safety policies
(IEEE, 2018-01-12) Göynügür, Emre; Şensoy, Murat; Mel, G. de; Computer Science; ŞENSOY, Murat; Göynügür, Emre
Monitoring and following health and safety regulations are especially important - but made difficult - in hazardous work environments such as underground mines to prevent work place accidents and illnesses. Even though there are IoT solutions for health and safety, every work place has different characteristics and monitoring is typically done by humans in control rooms. During emergencies, conflicts may arise among prohibitions and obligations, and humans may not be better placed to make decision without any assistance as they do not have a bird's-eye-view of the environment. Motivated by this observations, in this paper, we discuss how health and safety regulations can be implemented using a semantic policy framework. We then show how this framework can be integrated into an in-use smart underground mine solution. We also evaluate the performance of our framework to show that it can cope with the complexity and the amount of data generated by the system.
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Constrained min-cut replication for k-way hypergraph partitioning
(Informs, 2014) Yazıcı, Volkan; Aykanat, C.; Computer Science; YAZICI, Volkan
Replication is a widely-used technique in information retrieval and database systems for providing fault tolerance and reducing parallelization and processing costs. Combinatorial models based on hypergraph partitioning are proposed for various problems arising in information retrieval and database systems. We consider the possibility of using vertex replication to improve the quality of hypergraph partitioning. In this study, we focus on the constrained min-cut replication (CMCR) problem, where we are initially given a maximum replication capacity and a K-way hypergraph partition with an initial imbalance ratio. The objective in the CMCR problem is finding the optimal vertex replication sets for each part of the given partition such that the initial cut size of the partition is minimized, where the initial imbalance is either preserved or reduced under the given replication capacity constraint. In this study, we present a complexity analysis of the CMCR problem and propose a model based on a unique blend of coarsening and integer linear programming (ILP) schemes. This coarsening algorithm is derived from a novel utilization of the Dulmage-Mendelsohn decomposition. Experiments show that the ILP formulation coupled with the Dulmage-Mendelsohn decomposition-based coarsening provides high quality results in practical execution times for reducing the cut size of a given K-way hypergraph partition.
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Cooperative multi-task assignment for heterogonous UAVs
(IEEE, 2015) Özalp, N.; Ayan, U.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan
This research is focused on the cooperative multi-task assignment problem for heterogeneous UAVs, where a set of multiple tasks, each requiring a predetermined number of UAVs, have to be completed at specific locations. We modeled this as an optimization problem to minimize the number of uncompleted tasks while also minimizing total airtime and total distance traveled by all the UAVs. By taking into account the UAV flight capacities. For the solution of the problem, we adopted a multi-Traveling Salesman Problem (mTSP) method [1] and designed a new genetic structure for it so that it can be applied to cooperative multi-task assignment problems. Furthermore, we developed two domain specific mutation operators to improve the quality of the solutions in terms of number of uncompleted tasks, total airtime and total distance traveled by all the UAVs. The simulation experiments showed that these operators significantly improve the solution quality. Our main contributions are the application of the Multi Structure Genetic Algorithm (MSGA) to cooperative multi-task assignment problem and the development of two novel mutation operators to improve the solution of MSGA.
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CPU design simplified
(IEEE, 2018-12-10) Yıldız, A.; Uğurdağ, Hasan Fatih; Aktemur, Tankut Barış; İskender, Deniz; Gören, S.; Electrical & Electronics Engineering; Computer Science; UĞURDAĞ, Hasan Fatih; AKTEMUR, Tankut Bariş; İskender, Deniz
The first goal of this paper is to introduce a simple and customizable soft CPU named VerySimpleCPU (VSCPU), which could be easily implemented on FPGAs with a complete toolchain including instruction set simulator, assembler, and C compiler. The second goal is to offer to use this CPU as a teaching material within computer architecture/organization courses for students to understand the essentials and inner workings of a CPU better by designing a simple one. In addition to this, it is also aimed to teach writing code both in assembly level and C level for the CPU designed to understand what a compiler is and why it is needed.
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Data stream analytics and mining in the cloud
(IEEE, 2012) Arı, İsmail; Ölmezoğulları, Erdi; Çelebi, Ö. F.; Computer Science; ARI, Ismail; Ölmezoğulları, Erdi
Due to prevalent use of sensors and network monitoring tools, big volumes of data or “big data” today traverse the enterprise data processing pipelines in a streaming fashion. While some companies prefer to deploy their data processing infrastructures and services as private clouds, others completely outsource these services to public clouds. In either case, attempting to store the data first for subsequent analysis creates additional resource costs and unwanted delays in obtaining actionable information. As a result, enterprises increasingly employ data or event stream processing systems and further want to extend them with complex online analytic and mining capabilities. In this paper, we present implementation details for doing both correlation analysis and association rule mining (ARM) over streams. Specifically, we implement Pearson-Product Moment Correlation for analytics and Apriori & FPGrowth algorithms for stream mining inside a popular event stream processing engine called Esper. As a unique contribution, we conduct experiments and present performance results of these new tools with different tumbling and sliding time-windows over two different stream types: one for moving bus trajectories and another for web logs from a music site. We find that while tumbling windows may be more preferable for performance in certain applications, sliding windows can provide additional benefits with rule mining. We hope that our findings can shed light on the design of other cloud analytics systems.
Open Access
Deepsym: Deep symbol generation and rule learning for planning from unsupervised robot interaction
(AI Access Foundation, 2022) Ahmetoglu, A.; Seker, M. Y.; Piater, J.; Öztop, Erhan; Ugur, E.; Computer Science; ÖZTOP, Erhan
Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended environments. Therefore symbol formation and rule extraction should be considered part of robot learning, which, when done properly, will offer scalability, flexibility, and robustness. Towards this goal, we propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is assumed to be acquired earlier and observes the effects it can create in the environment. To form action-grounded object, effect, and relational categories, we employ a binary bottleneck layer in a predictive, deep encoder-decoder network that takes the image of the scene and the action applied as input, and generates the resulting effects in the scene in pixel coordinates. After learning, the binary latent vector represents action-driven object categories based on the interaction experience of the robot. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, a decision tree is trained to reproduce its decoder function. Probabilistic rules are extracted from the decision paths of the tree and are represented in the Probabilistic Planning Domain Definition Language (PPDDL), allowing off-the-shelf planners to operate on the knowledge extracted from the sensorimotor experience of the robot. The deployment of the proposed approach for a simulated robotic manipulator enabled the discovery of discrete representations of object properties such as 'rollable' and 'insertable'. In turn, the use of these representations as symbols allowed the generation of effective plans for achieving goals, such as building towers of the desired height, demonstrating the effectiveness of the approach for multi-step object manipulation. Finally, we demonstrate that the system is not only restricted to the robotics domain by assessing its applicability to the MNIST 8-puzzle domain in which learned symbols allow for the generation of plans that move the empty tile into any given position.
Open Access
Design and implementation of a cloud computing service for finite element analysis
(Elsevier, 2013-06) Arı, İsmail; Muhtaroğlu, Nitel; Computer Science; ARI, Ismail; Muhtaroğlu, Nitel
This paper presents an end-to-end discussion on the technical issues related to the design and implementation of a new cloud computing service for finite element analysis (FEA). The focus is specifically on performance characterization of linear and nonlinear mechanical structural analysis workloads over multi-core and multi-node computing resources. We first analyze and observe that accurate job characterization, tuning of multi-threading parameters and effective multi-core/node scheduling are critical for service performance. We design a “smart” scheduler that can dynamically select some of the required parameters, partition the load and schedule it in a resource-aware manner. We can achieve up to 7.53× performance improvement over an aggressive scheduler using mixed FEA loads. We also discuss critical issues related to the data privacy, security, accounting, and portability of the cloud service.
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Design for ARINC 653 conformance: architecting independent validation of a safety-critical RTOS
(IEEE, 2014) Alptekin, A.; Yilmazer, Y.; Usug, U.; Koca, F.; İnçki, Koray; İnçki, Koray
The ARINC 653 specification not only provides a standard application programming interface for an RTOS, but also specifies how to validate an ARINC 653 based RTOS. ARINC 653 Part 3 Conformity Test Specification specifies test procedures for validation of ARINC 653 Part 1 (Required Services Specification). Existing ARINC 653 verification suites and packs do not provide platform-independency, maintainability gained by an open source framework, a reliable communication protocol, and automated testing principles at the same time. This paper introduces a brand new validation suite, GVT-A653 which is platform-independent and ensures conformance to ARINC 653 specification. The suite is based on TETware (trademark of OpenGroup) and builds upon Continuous Integration (CI) principles. It also brings flexibility by providing various protocols including Avionics Full-Duplex Switched Ethernet (AFDX) Network that provides deterministic communication required in avionics applications.
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DNN-based speaker-adaptive postfiltering with limited adaptation data for statistical speech synthesis systems
(IEEE, 2019) Öztürk, M. G.; Ulusoy, O.; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk
Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional postfilters that are widely used in SPSS systems for increasing the quality of synthesized speech. However, existing DNN-based postfilters are trained with speaker-dependent databases. Given that SPSS systems can rapidly adapt to new speakers from generic models, there is a need for DNN-based postfilters that can adapt to new speakers with minimal adaptation data. Here, we compare DNN-, RNN-, and CNN-based postfilters together with adversarial (GAN) training and cluster-based initialization (CI) for rapid adaptation. Results indicate that the feedforward (FF) DNN, together with GAN and CI, significantly outperforms the other recently proposed postfilters.
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Dual camera based high spatio-temporal resolution video generation for wide area surveillance
(IEEE, 2022) Suluhan, Hasan Umut; Ates, H. F.; Gunturk, B. K.; Suluhan, Hasan Umut
Wide area surveillance (WAS) requires high spatiotemporal resolution (HSTR) video for better precision. As an alternative to expensive WAS systems, low-cost hybrid imaging systems can be used. This paper presents the usage of multiple video feeds for the generation of HSTR video as an extension of reference based super resolution (RefSR). One feed captures video at high spatial resolution with low frame rate (HSLF) while the other captures low spatial resolution and high frame rate (LSHF) video simultaneously for the same scene. The main purpose is to create an HSTR video from the fusion of HSLF and LSHF videos. In this paper we propose an end-to-end trainable deep network that performs optical flow (OF) estimation and frame reconstruction by combining inputs from both video feeds. The proposed architecture provides significant improvement over existing video frame interpolation and RefSR techniques in terms of PSNR and SSIM metrics and can be deployed on drones with dual cameras.
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Effort estimation for architectural refactoring of data tier software
(IEEE, 2022) Ersoy, E.; Sözer, Hasan; Computer Science; SÖZER, Hasan
Architectural refactoring requires substantial effort. We introduce an approach and a tool to predict this effort prior to refactoring. We focus on PL/SQL programs that are developed as data access tiers of business software. There are two types of common refactoring needs for these programs. First, some of the modules might need to be migrated to a separate database. Second, some of the modules in the data tier might need to be migrated to the application tier. In both cases, the refactoring effort is proportional to the amount of coupling between the migrated modules and the rest of the modules in the database. Our tool can parse PL/SQL programs to reveal this coupling based on an analysis of SQL queries. Unlike prior studies, our tool can analyze queries that are created dynamically and that use multiple tables as well as PL/SQL-specific features. We evaluate our approach with an industrial PL/SQL program from the telecommunications domain. Our results are approved to be accurate by domain experts.

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