WoS İndeksli Yayınlar Koleksiyonu

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

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Now showing 1 - 4 of 4
  • 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 - 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: 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: 17
    Citation - Scopus: 20
    In Search of the Origins and Enduring Impact of Agile Software Development
    (Assoc Computing Machinery, 2018) Clarke, Paul; O'Connor, Rory, V; Yilmaz, Murat
    The Agile Manifesto is a philosophical touchpoint for all agile software development (ASD) methods. We examine the manifesto and certain agile methods in an effort to identify the major impacts of ASD. We have encountered some difficulty in delineating agile and non-agile software processes, which is partially the result of terminological confusion. It is clear from the volume of published research that ASD has made a significant contribution, and we have identified two lasting impacts: a reduction in iteration durations and a push for reduced levels of documentation. We find that the contemporary use of tooling to automate processes may not be wholly congruent with the manifesto and that many organisations may still rely on business contracts despite calls in the manifesto for greater levels of informal customer collaboration.