Computer Science

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

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Now showing 1 - 20 of 70

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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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Authoring and presentation tools for distance learning over interactive TV
(ACM, 2010) Gürel, T. C.; Erdem, Tanju; Kermen, A.; Özkan, M. K.; Eroğlu Erdem, Ç.; Computer Science; ERDEM, Arif Tanju
We present a complete system for distance learning over interactive TV with novel tools for authoring and presentation of lectures and exams, and evaluation of student and system performance. The main technological contributions of the paper include the development of plug-in software so that PowerPoint can be used to prepare presentations for the set-top-box, a software tool to convert PDF documents containing multiple-choice questions into interactive exams, and a virtual teacher whose facial animation is automatically generated from speech.
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Bidding support by the pocket negotiator improves negotiation outcomes
(Springer, 2023) Aydoğan, Reyhan; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
This paper presents the negotiation support mechanisms provided by the Pocket Negotiator (PN) and an elaborate empirical evaluation of the economic decision support (EDS) mechanisms during the bidding phase of negotiations as provided by the PN. Some of these support mechanisms are offered actively, some passively. With passive support we mean that the user only gets that support by clicking a button, whereas active support is provided without prompting. Our results show, that PN improves negotiation outcomes, counters cognitive depletion, and encourages exploration of potential outcomes. We found that the active mechanisms were used more effectively than the passive ones and, overall, the various mechanisms were not used optimally, which opens up new avenues for research. As expected, the participants with higher negotiation skills outperformed the other groups, but still they benefited from PN support. Our experimental results show that people with enough technical skills and with some basic negotiation knowledge will benefit most from PN support. Our results also show that the cognitive depletion effect is reduced by Pocket Negotiator support. The questionnaire taken after the experiment shows that overall the participants found Pocket Negotiator easy to interact with, that it made them negotiate more quickly and that it improves their outcome. Based on our findings, we recommend to 1) provide active support mechanisms (push) to nudge users to be more effective, and 2) provide support mechanisms that shield the user from mathematical complexities.
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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.
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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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Cooperation and trust in the presence of bias
(2014) Şensoy, Murat; Computer Science; Cohen, R.; Falcone, R.; Norman, T.; ŞENSOY, Murat
Stereotypes may influence the attitudes that individuals have towards others. Stereotypes, therefore, represent biases toward and against others. In this paper, we formalise stereotypical bias within trust evaluations. Then, using the iterated prisoners’ dilemma game, we quantitatively analyse how cooperation and mutual trust between self-interested agents are affected by stereotypical bias. We present two key findings: i) stereotypical bias of one player may inhibit cooperation by creating incentives for others to defect, ii) even if only one of the players has a stereotypical bias, convergence of mutual trust between players may be strictly determined by the bias.
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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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Darbe i̇şaretleri̇ i̇çi̇n aşırı-hızlı FPGA tabanlı eǧri̇ beti̇mlenmesi̇
(IEEE, 2012) Başaran, A.; Uğurdağ, Hasan Fatih; Akdoğan, T.; Güney, V. U.; Gören, S.; Electrical & Electronics Engineering; UĞURDAĞ, Hasan Fatih
Bu çalışmada anlatılan donanım 1.5 GHz Analog-Sayısal- Çevirici’den gelen darbe işaret dizisini işleyebilmekte ve darbe işaretlerini genlik, yükselme/düşme süresi ve varış zamanı parametreleriyle özetleyebilmektedir. Söz konusu donanım arka arkaya gelen sıfır ölü-süreli darbe işaretlerini işleyebilmektedir. Darbeler 9 örnek kadar kısa olabilmektedir. Bu şekildeki darbe işaretleri (ve hatta birçok kanallı olanları) parçacıkların hızlandırılıp, çarpıştırılıp ve algılandıkları yüksek enerji fizik deneylerinde bulunmaktadır. Benzer fiziksel kurulumlar nükleer tıp imgelemede, özellikle Pozitron Salımı Tomografisi’nde de mevcuttur. Donanım gerçeklemesini Sahada Programlanabilir Kapı Dizileri (FPGA) üzerinde yaptık. FPGA’lar yüksek düzeyde paralellik ve bunun sonucu yüksek veri işleme gücü sunmaktadırlar. Bu bildiride, üst seviye mimari, ara yapı detayları, tasarım süreci ve gerçekleme detayları sunulmaktadır. FPGA tasarımımız sayesinde ilgili uygulamalarda alan, güç, devreye alınma zamanı ve operasyonel maliyet tasarrufu sağlamak mümkündür.
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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.
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Developmental scaffolding with large language models
(IEEE, 2023) Çelik, B.; Ahmetoglu, A.; Ugur, E.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan
Exploration and self-observation are key mechanisms of infant sensorimotor development. These processes are further guided by parental scaffolding to accelerate skill and knowledge acquisition. In developmental robotics, this approach has been adopted often by having a human acting as the source of scaffolding. In this study, we investigate whether Large Language Models (LLMs) can act as a scaffolding agent for a robotic system that aims to learn to predict the effects of its actions. To this end, an object manipulation setup is considered where one object can be picked and placed on top of or in the vicinity of another object. The adopted LLM is asked to guide the action selection process through algorithmically generated state descriptions and action selection alternatives in natural language. The simulation experiments that include cubes in this setup show that LLM-guided (GPT3.5-guided) learning yields significantly faster discovery of novel structures compared to random exploration. However, we observed that GPT3.5 fails to effectively guide the robot in generating structures with different affordances such as cubes and spheres. Overall, we conclude that even without fine-tuning, LLMs may serve as a moderate scaffolding agent for improving robot learning, however, they still lack affordance understanding which limits the applicability of the current LLMs in robotic scaffolding tasks.
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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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E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing
(ACM, 2011-06-12) Liu, M.; Rundensteiner, E.; Greenfield, K.; Gupta, C.; Wang, S.; Arı, İsmail; Mehta, A.; Computer Science; ARI, Ismail
Many modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for event stream processing technology to analyze this vast amount of sequential data to enable online operational decision making. Existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for sequence detection do not support OLAP operations. We propose a novel E-Cube model which combines CEP and OLAP techniques for efficient multi-dimensional event pattern analysis at different abstraction levels. Our analysis of the interrelationships in both concept abstraction and pattern refinement among queries facilitates the composition of these queries into an integrated E-Cube hierarchy. Based on this E-Cube hierarchy, strategies of drill-down (refinement from abstract to more specific patterns) and of roll-up (generalization from specific to more abstract patterns) are developed for the efficient workload evaluation. Our proposed execution strategies reuse intermediate results along both the concept and the pattern refinement relationships between queries. Based on this foundation, we design a cost-driven adaptive optimizer called Chase, that exploits the above reuse strategies for optimal E-Cube hierarchy execution. Our experimental studies comparing alternate strategies on a real world financial data stream under different workload conditions demonstrate the superiority of the Chase method. In particular, our Chase execution in many cases performs ten fold faster than the state-of-the art strategy for real stock market query workloads.
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Effect of awareness of other side’s gain on negotiation outcome, emotion, argument, and bidding behavior
(Springer, 2021) Güngör, Onat; Çakan, Umut; Aydoğan, Reyhan; Öztürk, P.; Computer Science; AYDOĞAN, Reyhan; Güngör, Onat; Çakan, Umut
Designing agents aiming to negotiate with human counterparts requires additional factors. In this work, we analyze the main elements of human negotiations in a structured human experiment. Particularly, we focus on studying the effect of negotiators being aware of the other side’s gain on the bidding behavior and the negotiation outcome. We compare the negotiations in two settings where one allows human negotiators to see their opponent’s utility and the other does not. Furthermore, we study what kind of emotional state expressed and arguments sent in those setups. We rigorously discuss the findings from our experiments.
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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.
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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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Eklemeli̇ di̇ller i̇çi̇n düşük bellekli̇ melez i̇stati̇sti̇ksel/bi̇ri̇m seçmeli̇ MKS si̇stemi̇
(IEEE, 2012) Guner, Ekrem; Demiroğlu, Cenk; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Guner, Ekrem
The HMM-based TTS (HTS) approach has been increasingly getting more attention from the TTS research community. One of the advantage is the lack of spurious errors that are observed in the unit selection scheme. Another advantage of the HTS system is the small memory footprint requirement which makes it attractive for embedded devices. Here, we propose a novel hybrid statistical unit selection TTS system for agglutinative languages that aims at improving the quality of the baseline HTS system while keeping the memory footprint small. The intelligibility and quality scores of the baseline system are comparable to the MOS scores of English reported in the Blizzard Challenge tests. Listeners preferred the hybrid system over the baseline system in the A/B preference tests.

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