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ArticlePublication Open Access The effect of appearance of virtual agents in human-agent negotiation(MDPI, 2022-09) Türkgeldi, Berkay; Özden, Cana Su; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Türkgeldi, Berkay; Özden, Cana SuArtificial Intelligence (AI) changed our world in various ways. People start to interact with a variety of intelligent systems frequently. As the interaction between human and AI systems increases day by day, the factors influencing their communication have become more and more important, especially in the field of human-agent negotiation. In this study, our aim is to investigate the effect of knowing your negotiation partner (i.e., opponent) with limited knowledge, particularly the effect of familiarity with the opponent during human-agent negotiation so that we can design more effective negotiation systems. As far as we are aware, this is the first study investigating this research question in human-agent negotiation settings. Accordingly, we present a human-agent negotiation framework and conduct a user experiment in which participants negotiate with an avatar whose appearance and voice are a replica of a celebrity of their choice and with an avatar whose appearance and voice are not familiar. The results of the within-subject design experiment show that human participants tend to be more collaborative when their opponent is a celebrity avatar towards whom they have a positive feeling rather than a non-celebrity avatar.Conference ObjectPublication Open Access Evidential deep learning to quantify classification uncertainty(Neural Information Processing Systems Foundation, 2018) Şensoy, Murat; Kaplan, L.; Kandemir, M.; Computer Science; ŞENSOY, MuratDeterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of out-of-distribution queries and endurance against adversarial perturbations.
Conference ObjectPublication Metadata only COSMOS on steroids: a Cheap detector for cheapfakes(The ACM Digital Library, 2021) Akgül, T.; Civelek, Tuğçe Erkılıç; Uğur, Deniz; Beğen, Ali Cengiz; Computer Science; BEĞEN, Ali Cengiz; Civelek, Tuğçe Erkılıç; Uğur, DenizThe growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors developed a self-supervised training strategy that detected whether different captions for a given image were out-of-context, meaning that even though pointing to the same object(s) in the image, the captions implied different meanings. In this paper, we propose four methods to improve the detection accuracy of COSMOS. These methods range from differential sensing and fake-or-fact checking that detect contradicting or fake captions to object-caption matching and threshold adjustment that modify the baseline algorithm for improved accuracy.ArticlePublication Metadata only A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance(Elsevier, 2019-03) Sanchez-Anguix, V.; Chalumuri, R.; Aydoğan, Reyhan; Julian, V.; Computer Science; AYDOĞAN, ReyhanThe problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors' preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the studentsupervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.ArticlePublication Metadata only Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems(The ACM Digital Library, 2019-06) Kıraç, Mustafa Furkan; Aktemur, Tankut Barış; Sözer, Hasan; Gebizli, C. Ş.; Computer Science; KIRAÇ, Mustafa Furkan; AKTEMUR, Tankut Bariş; SÖZER, HasanWe propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model together with manually-applied test cases for robustness testing. We use this combination to train a long short-term memory (LSTM) network. As a result, the network learns an implicit representation of the usage behavior that is liable to failures. We use this network to generate new event sequences as test cases. We applied our approach in the context of an industrial case study for the black-box testing of a digital TV system. LSTM-generated test cases were able to reveal several faults, including critical ones, that were not detected with existing automated or manual testing activities. Our approach is complementary to model-based and exploratory testing, and the combined approach outperforms random testing in terms of both fault coverage and execution time.Conference ObjectPublication Metadata only Dağıtık bir openflow kontrol birimi mimarisi(IEEE, 2012) Yazıcı, Volkan; Sunay, Mehmet Oğuz; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; SUNAY, Mehmet Oğuz; YAZICI, VolkanConsidering the modern internet traffic rates, the network architecture is of particular importance as the running services itself. On the other hand, due to the increasing complexity and black-box structure of the available networking hardware (switches, routers, etc.), the necessary network innovation imposed by the running services becomes infeasible in practice. The software-defined networking notion introduced to solve this problem and one of its emerging and powerful implementations, the OpenFlow protocol, advocate the idea of providing the control and data paths in separate planes. A network operating system running on this control plane, is anticipated to provide necessary measures for scalability and reliability in order to stand against the gigantic traffic pumped by the network. In this paper, we propose a distributed OpenFlow network operating system built with necessary scalability and reliability qualifications without requiring any changes to the existing OpenFlow protocol and networking equipment.ArticlePublication Metadata only A longitudinal case study on Nexus transformation: Impact on productivity, quality, and motivation(Wiley, 2023-09) Ersoy, E.; Çallı, E.; Erdoğan, B.; Bağrıyanık, S.; Sözer, Hasan; Computer Science; SÖZER, HasanThere have been success stories reported regarding the adoption of agile software development methods in the industry. There also exist observations on their limitations. One of these limitations is scalability since agile methods like Scrum were originally designed for small software teams. Scalable agile frameworks were introduced to address this limitation. We conducted an industrial case study on the adoption of such a framework, called Nexus. Our study involves quantitative and qualitative evaluation based on observations within a product development organization over a period of 12 months. Scrum is used for the development of a product during the first 6 months of this period. Nexus is used in the remaining 6 months. Data are collected throughout the whole period for measuring productivity, quality, and team member motivation. Results suggest a significant increase in productivity and product quality after switching to Nexus. Team motivation was slightly improved as well.ArticlePublication Metadata only On characterizing sectoral interactions via connections between employees in professional online social networks(Elsevier, 2018-12) Ayvaz, Demet; Gürsun, Gonca; Özlale, Ümit; Economics; Computer Science; GÜRSUN, Gonca; ÖZLALE, Ümit; Ayvaz, DemetThe collaboration among individuals is essential to maximize economic efficiency. Today most of the technological and economical advancements require multidisciplinary efforts. Therefore promoting interaction and knowledge sharing between industry sectors within a country is more crucial than ever. One main platform for such communication is business-oriented online social networks where thousands of professionals from various sectors connect with each other. These social networks provide a way of disseminating the latest information in technology and business. Our goal in this paper is to analyze the connectivity patterns of individuals in a business-oriented social network as a tool to understand how industry sectors are represented and interact with each other in such online platforms. To do that, we collect profiles of thousands of employees from a professional online social network. Then, first, we analyze the structural properties of the network and report its characteristics in comparison with the non-professional ones. Second, we map each employee to the sector she works in and study the connectivity patterns within each sector separately. We find that the connectivity patterns within sectors vary and the employees within a sector do not necessarily form densely connected communities. Third, we investigate the relationship between sectors via the connectivity of their employees and identify the main social clusters of sectors. We show that there are significant similarities between social connectivity and the economic transactions between sectors.Conference ObjectPublication Metadata only Summary of an effective formulation of the multi-criteria test suite minimization problem(IEEE, 2022) Özener, Okan Örsan; Sözer, Hasan; Industrial Engineering; Computer Science; ÖZENER, Okan Örsan; SÖZER, HasanThis is an extended abstract of the article: Okan Orsan Ozener and Hasan Sozer, 'An Effective Formulation of the Multi-Criteria Test Suite Minimization Problem', published in the Journal of Systems and Software, Vol. 168, pp. 110632, 2020. https://doi.org/10.1016/j.jss.2020.110632.ArticlePublication Open Access ProbC: joint modeling of epigenome and transcriptome effects in 3D genome(BioMed Central Ltd, 2022-12) Sefer, Emre; Computer Science; SEFER, EmreBackground: Hi-C and its high nucleosome resolution variant Micro-C provide a window into the spatial packing of a genome in 3D within the cell. Even though both techniques do not directly depend on the binding of specific antibodies, previous work has revealed enriched interactions and domain structures around multiple chromatin marks; epigenetic modifications and transcription factor binding sites. However, the joint impact of chromatin marks in Hi-C and Micro-C interactions have not been globally characterized, which limits our understanding of 3D genome characteristics. An emerging question is whether it is possible to deduce 3D genome characteristics and interactions by integrative analysis of multiple chromatin marks and associate interactions to functionality of the interacting loci. Result: We come up with a probabilistic method ProbC to decompose Hi-C and Micro-C interactions by known chromatin marks. ProbC is based on convex likelihood optimization, which can directly take into account both interaction existence and nonexistence. Through ProbC, we discover histone modifications (H3K27ac, H3K9me3, H3K4me3, H3K4me1) and CTCF as particularly predictive of Hi-C and Micro-C contacts across cell types and species. Moreover, histone modifications are more effective than transcription factor binding sites in explaining the genome’s 3D shape through these interactions. ProbC can successfully predict Hi-C and Micro-C interactions in given species, while it is trained on different cell types or species. For instance, it can predict missing nucleosome resolution Micro-C interactions in human ES cells trained on mouse ES cells only from these 5 chromatin marks with above 0.75 AUC. Additionally, ProbC outperforms the existing methods in predicting interactions across almost all chromosomes. Conclusion: Via our proposed method, we optimally decompose Hi-C interactions in terms of these chromatin marks at genome and chromosome levels. We find a subset of histone modifications and transcription factor binding sites to be predictive of both Hi-C and Micro-C interactions and TADs across human, mouse, and different cell types. Through learned models, we can predict interactions on species just from chromatin marks for which Hi-C data may be limited.