WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Now showing 1 - 10 of 12
  • Conference Object
    Citation - WoS: 5
    Citation - Scopus: 6
    Err@hri 2024 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Interactions
    (Assoc Computing Machinery, 2024) Spitale, Micol; Parreira, Maria Teresa; Stiber, Maia; Axelsson, Minja; Kara, Neval; Kankariyat, Garima; Gunes, Hatice; Kankariya, Garima
    Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as interrupting people or having delayed responses, as well as their limited ability to understand human speech, i.e., failure in tasks like transcribing speech to text. These mistakes may disrupt interactions and negatively influence human perception of these robots. To address this problem, robots need to have the ability to detect human-robot interaction (HRI) failures. The ERR@HRI 2024 challenge tackles this by offering a benchmark multimodal dataset of robot failures during human-robot interactions, encouraging researchers to develop and benchmark multimodal machine learning models to detect these failures. We created a dataset featuring multimodal non-verbal interaction data, including facial, speech, and pose features from video clips of interactions with a robotic coach, annotated with labels indicating the presence or absence of robot mistakes, user awkwardness, and interaction ruptures, allowing for the training and evaluation of predictive models. Challenge participants have been invited to submit their multimodal ML models for detection of robot errors, to be evaluated against various performance metrics such as accuracy, precision, recall, F1 score, with and without a margin of error reflecting the time-sensitivity of these metrics. The results of this challenge will help the research field in better understanding the robot failures in human-robot interactions and designing autonomous robots that can mitigate their own errors after successfully detecting them.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Empathy Development in Digital Accessibility Through Real-Life Practices in a Programming Course: a Case Study
    (Assoc Computing Machinery, 2024) Inal, Yavuz; Cagiltay, Nergiz
    This case study adopted a project-based learning approach to a programming course based on real-life practices to help software engineering students develop empathy skills regarding digital accessibility. A project was assigned to first-year students to develop software for people with disabilities. The data were collected from each individual project of thirty-three students over four months. Students' efforts regarding analysis, design and development steps, and project outcomes were analyzed. The study results showed that students' experience level and knowledge about the accessibility domain were quite low initially. Regarding the target disability type in their projects, half of the students selected mental illness, followed by blindness, deafness, and physical illness. The students who gathered requirements from domain experts or target users made their products more accessible, indicating the importance of user involvement in empathy building in the development process. We also measured increased awareness of and knowledge about accessibility at the end of the course, leading us to discuss the effectiveness of real-life practices in teaching digital accessibility in programming courses.
  • Conference Object
    Citation - WoS: 8
    Citation - Scopus: 11
    Sentiment Analysis for the Social Media: a Case Study for Turkish General Elections
    (Assoc Computing Machinery, 2017) Yumusak, Semih; Oztoprak, Kasim; Dogdu, Erdogan; Uysal, Elif
    The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specific keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results.
  • Conference Object
    Citation - WoS: 40
    Citation - Scopus: 77
    Malware Classification Using Deep Learning Methods
    (Assoc Computing Machinery, 2018) Dogdu, Erdogan; Cakir, Bugra
    Malware, short for Malicious Software, is growing continuously in numbers and sophistication as our digital world continuous to grow. It is a very serious problem and many efforts are devoted to malware detection in today's cybersecurity world. Many machine learning algorithms are used for the automatic detection of malware in recent years. Most recently, deep learning is being used with better performance. Deep learning models are shown to work much better in the analysis of long sequences of system calls. In this paper a shallow deep learning-based feature extraction method (word2vec) is used for representing any given malware based on its opcodes. Gradient Boosting algorithm is used for the classification task. Then, k-fold cross-validation is used to validate the model performance without sacrificing a validation split. Evaluation results show up to 96% accuracy with limited sample data.
  • Conference Object
    Citation - Scopus: 2
    A Discovery and Analysis Engine for Semantic Web
    (Assoc Computing Machinery, 2018) Kamilaris, Andreas; Dogdu, Erdogan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre; Yumusak, Semih
    The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.
  • Conference Object
    Citation - WoS: 7
    Phishing E-Mail Detection by Using Deep Learning Algorithms
    (Assoc Computing Machinery, 2018) Hassanpour, Reza; Dogdu, Erdogan; Choupani, Roya; Goker, Onur; Nazli, Nazli
  • Conference Object
    Citation - WoS: 30
    Citation - Scopus: 51
    An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework
    (Assoc Computing Machinery, 2017) Ozbayoglu, A. Murat; Dogdu, Erdogan; Sezer, Omer Berat
    In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.
  • Conference Object
    Citation - WoS: 11
    Citation - Scopus: 13
    Adopting Virtual Reality as a Medium for Software Development Process Education
    (Assoc Computing Machinery, 2018) Isler, Veysi; O'Connor, Rory, V; Clarke, Paul; Gulec, Ulas; Yilmaz, Murat
    Software development is a complex process of collaborative endeavour which requires hands-on experience starting from requirement analysis through to software testing and ultimately demands continuous maintenance so as to mitigate risks and uncertainty. Therefore, training experienced software practitioners is a challenging task. To address this gap, we propose an interactive virtual reality training environment for software practitioners to gain virtual experience based on the tasks of software development. The goal is to transport participants to a virtual software development organization where they experience simulated development process problems and conflicting situations, where they will interact virtually with distinctive personalities, roles and characters borrowed from real software development organizations. This PhD in progress paper investigates the literature and proposes a novel approach where participants can acquire important new process knowledge. Our preliminary observations suggest that a complementary VR-based training tool is likely to improve the experience of novice software developers and ultimately it has a great potential for training activities in software development organizations.
  • Conference Object
    Analysis of Neurooncological Data To Predict Success of Operation Through Classification
    (Assoc Computing Machinery, 2016) Tokdemir, Gul; Cagiltay, Nergiz; Maras, H. Hakan; Bagherzadi, Negin; Borcek, Alp Ozgun
    Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.
  • Conference Object
    Citation - WoS: 140
    Citation - Scopus: 214
    Intrusion Detection Using Big Data and Deep Learning Techniques
    (Assoc Computing Machinery, 2019) Dogdu, Erdogan; Faker, Osama
    In this paper, Big Data and Deep Learning Techniques are integrated to improve the performance of intrusion detection systems. Three classifiers are used to classify network traffic datasets, and these are Deep Feed-Forward Neural Network (DNN) and two ensemble techniques, Random Forest and Gradient Boosting Tree (GBT). To select the most relevant attributes from the datasets, we use a homogeneity metric to evaluate features. Two recently published datasets UNSW NB15 and CICIDS2017 are used to evaluate the proposed method. 5-fold cross validation is used in this work to evaluate the machine learning models. We implemented the method using the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library to implement the deep learning technique while the ensemble techniques are implemented using Apache Spark Machine Learning Library. The results show a high accuracy with DNN for binary and multiclass classification on UNSW NB15 dataset with accuracies at 99.16% for binary classification and 97.01% for multiclass classification. While GBT classifier achieved the best accuracy for binary classification with the CICIDS2017 dataset at 99.99%, for multiclass classification DNN has the highest accuracy with 99.56%.