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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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.
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.
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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Enabling smart environments through scalable policy reasoning and Internet of Things
(Wiley, 2019-04) Göynügür, Emre; Şensoy, Murat; de Mel, G.; Computer Science; ŞENSOY, Murat; Göynügür, Emre
In this paper, we discuss how to combine ontology-based policy reasoning mechanisms with in-use Internet of Things applications to customize and automate device behaviors. We discuss how the policy framework can be extended with data federation to handle diverse and distributed data sources. We demonstrate that smart devices and sensors can be orchestrated through policies in diverse settings, from smart home environments to hazardous workplaces, such as coal mines. Lastly, we evaluate our approach using real applications with real data and demonstrate that it is scalable under high load of data and devices.
Open Access
Evaluating the English-Turkish parallel treebank for machine translation
(TÜBİTAK, 2022) Görgün, O.; Yıldız, Olcay Taner; Computer Science; YILDIZ, Olcay Taner
This study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator’s behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by creating Nokia English-Turkish Treebank (NTB) to address technical document translation tasks. NTB also includes 8.3K sentences in varying lengths. We validate the corpus both extrinsically and intrinsically, and report our evaluation results regarding perplexity analysis and translation task results. Results prove that our heuristics yield promising results in terms of perplexity and are suitable for translation tasks in terms of BLEU scores.
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Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms
(Springer Science+Business Media, 2014-01) Turduev, M.; Cabrita, G.; Kırtay, Murat; Gazi, V.; Marques, L.; Kırtay, Murat
In this article we describe implementations of various bio-inspired algorithms for obtaining the chemical gas concentration map of an environment filled with a contaminant. The experiments are performed using Khepera III and miniQ miniature mobile robots equipped with chemical gas sensors in an environment with ethanol gas. We implement and investigate the performance of decentralized and asynchronous particle swarm optimization (DAPSO), bacterial foraging optimization (BFO), and ant colony optimization (ACO) algorithms. Moreover, we implement sweeping (sequential search algorithm) as a base case for comparison with the implemented algorithms. During the experiments at each step the robots send their sensor readings and position data to a remote computer where the data is combined, filtered, and interpolated to form the chemical concentration map of the environment. The robots also exchange this information among each other and cooperate in the DAPSO and ACO algorithms. The performance of the implemented algorithms is compared in terms of the quality of the maps obtained and success of locating the target gas sources.
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Extraction of novel features based on histograms of MFCCs used in emotion classification from generated original speech dataset
(2020) Pakyurek, M.; Atmış, Mahir; Kulac, S.; Uludag, U.; Atmış, Mahir
This paper introduces two significant contributions: one is a new feature based on histograms of MFCC (Mel-Frequency Cepstral Coefficients) extracted from the audio files that can be used in emotion classification from speech signals, and the other – our new multi-lingual and multi-personal speech database, which has three emotions. In this study, Berlin Database (BD) (in German) and our custom PAU database (in English) created from YouTube videos and popular TV shows are employed to train and evaluate the test results. Experimental results show that our proposed features lead to better classification of results than the current state-of-the-art approaches with Support Vector Machine (SVM) from the literature. Thanks to our novel feature, this study can outperform a number of MFCC features and SVM classifier based studies, including recent researches. Due to the lack of our novel feature based approaches, one of the most common MFCC and SVM framework is implemented and one of the most common database Berlin DB is used to compare our novel approach with these kind of approaches.
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Genetic algorithms and heuristics hybridized for software architecture recovery
(Springer, 2023-06-26) Elyasi, Milad; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah; Ekici, Ali; Özener, Okan Örsan; Sözer, Hasan; Industrial Engineering; Computer Science; EKİCİ, Ali; ÖZENER, Okan Örsan; SÖZER, Hasan; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah; Elyasi, Milad
Large scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.
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High-level features for resource economy and fast learning in skill transfer
(Taylor & Francis, 2022) Ahmetoglu, A.; Uğur, E.; Asada, M.; Öztop, Erhan; Computer Science; ÖZTOP, Erhan
Abstraction is an important aspect of intelligence which enables agents to construct robust representations for effective and efficient decision making. Although, deep neural networks are proven to be effective learning systems due to their ability to form increasingly complex abstractions at successive layers these abstractions are mostly distributed over many neurons, making the re-use of a learned skill costly and blind to the insights that can be obtained on the emergent representations. For avoiding designer bias and unsparing resource use, we propose to exploit neural response dynamics to form compact representations to use in skill transfer. For this, we consider two competing methods based on (1) maximum information compression principle and (2) the notion that abstract events tend to generate slowly changing signals, and apply them to the neural signals generated during task execution. To be concrete, in our simulation experiments, we either apply principal component analysis (PCA) or slow feature analysis (SFA) on the signals collected from the last hidden layer of a deep neural network while it performs a source task, and use these features for skill transfer in a new, target, task. We then compare the generalization and learning performance of these alternatives with the baselines of skill transfer with full layer output and no-transfer settings. Our experimental results on a simulated tabletop robot arm navigation task show that units that are created with SFA are the most successful for skill transfer. SFA as well as PCA, incur less resources compared to usual skill transfer where full layer outputs are used in the new task learning, whereby many units formed show a localized response reflecting end-effector-obstacle-goal relations. Finally, SFA units with the lowest eigenvalues resemble symbolic representations that highly correlate with high-level features such as joint angles and end-effector position which might be thought of as precursors for fully symbolic systems.
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InfraGAN: A GAN architecture to transfer visible images to infrared domain
(Elsevier, 2022-03) Özkanoglu, M. A.; Özer, Sedat; Computer Science; ÖZER, Sedat
Utilizing both visible and infrared (IR) images in various deep learning based computer vision tasks has been a recent trend. Consequently, datasets having both visible and IR image pairs are desired in many applications. However, while large image datasets taken at the visible spectrum can be found in many domains, large IR-based datasets are not easily available in many domains. The lack of IR counterparts of the available visible image datasets limits existing deep algorithms to perform on IR images effectively. In this paper, to overcome with that challenge, we introduce a generative adversarial network (GAN) based solution and generate the IR equivalent of a given visible image by training our deep network to learn the relation between visible and IR modalities. In our proposed GAN architecture (InfraGAN), we introduce using structural similarity as an additional loss function. Furthermore, in our discriminator, we do not only consider the entire image being fake or real but also each pixel being fake or real. We evaluate our comparative results on three different datasets and report the state of the art results over five metrics when compared to Pix2Pix and ThermalGAN architectures from the literature. We report up to +16% better performance in Structural Similarity Index Measure (SSIM) over Pix2Pix and +8% better performance over ThermalGAN for VEDAI dataset. Further gains on different metrics and on different datasets are also reported in our experiments section.
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Introduction of a spatio-temporal mapping based POE method for outdoor spaces: Suburban university campus as a case study
(Elsevier, 2018-11) Göçer, Özgür; Göçer, K.; Başol, Altuğ Melik; Kıraç, Mustafa Furkan; Özbil, A.; Bakovic, M.; Siddiqui, Faizan Pervez; Özcan, Barış; Computer Science; Architecture; Mechanical Engineering; BAŞOL, Altuğ Melik; KIRAÇ, Mustafa Furkan; GÖÇER, Özgür; Siddiqui, Faizan Pervez; Özcan, Barış
Outdoor spaces are important to sustainable cities because they establish a common identity for social life by improving quality of urban living. The relation between outdoor spaces and building groups, competency, use period, and interaction of micro-climatic factors are needed to be investigated from a holistic approach. Unfortunately, the limited and narrow scoped POE studies on outdoor spaces make an overall assessment without causality relation. Other existing studies in outdoor spaces are mostly grouped under the headings such as; user satisfaction, space syntax and behavioral mapping, and biometeorological assessments. The intention of this paper is to introduce a new post-occupancy evaluation (POE) method integrates these studies focusing on various problems in outdoor spaces using spatio-temporal mapping. The comprehensive methodology applied in this research attempted to overcome some of the shortcomings of related studies by conducting a longitudinal study (during a year, as opposed to a few days) and also by objectively analyzing the associations of user behavior and physical attributes as well as the configurational properties of the campus layout. With this method, outdoor spaces can be evaluated in the context of the interaction between the physical environment and its users' behavior and activities, level of satisfaction and perceptions of comfort. The method has been applied on a suburban university campus in İstanbul, Turkey. The main courtyard of the campus has been subjected for map creation and result discussions.
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Pedestrian tracking in outdoor spaces of a suburban university campus for the investigation of occupancy patterns
(Elsevier, 2019-02) Göçer, Özgür; Göçer, K.; Özcan, Barış; Bakovic, M.; Kıraç, Mustafa Furkan; Computer Science; Architecture; GÖÇER, Özgür; KIRAÇ, Mustafa Furkan; Özcan, Barış
The design of a livable and comfortable environment has been one of the main aims of sustainable university campus design. The creation of outdoor spaces for accommodating amenities has a positive effect on users with regard to various physiological and psychological aspects. Knowing how daily activity patterns and pedestrian movements are distributed across space is important for assessing whether or not human use and design plans are in fact successful. The aim of this study is to determine occupancy patterns and pedestrian routes in outdoor spaces during different seasons at a sustainable university campus by using spatial statistical analyses that involve ANN, MC and SDE. To perform these analyses, the researchers attempted to use a pedestrian tracking method from camera surveillance to aggregate the required data by conducing a longitudinal study. The data that were aggregated by pedestrian tracking was visualized with the use of a spatio-temporal mapping method in GIS. Logistic GWR was performed to seek the relationship between occupancy pattern (clustered distribution) and design layout of open spaces, comprising the variables of proximity to the attraction centers/entrances, and visual integration. The results confirmed that occupants prefer to use the areas that have high visual integration value and are close to attraction centers.
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SiameseFuse: A computationally efficient and a not-so-deep network to fuse visible and infrared images
(Elsevier, 2022-09) Özer, Sedat; Ege, M.; Özkanoglu, M. A.; Computer Science; ÖZER, Sedat
Recent developments in pattern analysis have motivated many researchers to focus on developing deep learning based solutions in various image processing applications. Fusing multi-modal images has been one such application area where the interest is combining different information coming from different modalities in a more visually meaningful and informative way. For that purpose, it is important to first extract salient features from each modality and then fuse them as efficiently and informatively as possible. Recent literature on fusing multi-modal images reports multiple deep solutions that combine both visible (RGB) and infra-red (IR) images. In this paper, we study the performance of various deep solutions available in the literature while seeking an answer to the question: “Do we really need deeper networks to fuse multi-modal images?” To have an answer for that question, we introduce a novel architecture based on Siamese networks to fuse RGB (visible) images with infrared (IR) images and report the state-of-the-art results. We present an extensive analysis on increasing the layer numbers in the architecture with the above-mentioned question in mind to see if using deeper networks (or adding additional layers) adds significant performance in our proposed solution. We report the state-of-the-art results on visually fusing given visible and IR image pairs in multiple performance metrics, while requiring the least number of trainable parameters. Our experimental results suggest that shallow networks (as in our proposed solutions in this paper) can fuse both visible and IR images as well as the deep networks that were previously proposed in the literature (we were able to reduce the total number of trainable parameters up to 96.5%, compare 2,625 trainable parameters to the 74,193 trainable parameters).
Open Access
Symbolic knowledge extraction for explainable nutritional recommenders
(Elsevier, 2023-06) Magnini, M.; Ciatto, G.; Cantürk, Furkan; Aydoğan, Reyhan; Omicini, A.; Computer Science; AYDOĞAN, Reyhan; Cantürk, Furkan
Background and objective: This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. Conclusions Thanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.
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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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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