Publications without Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
Browse
80 results
Search Results
Publication Metadata only On the rate of convergence of a classifier based on a transformer encoder(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Gurevych, Iryna; Kohler, Michael; Department of Computer Engineering; Şahin, Gözde Gül; Faculty Member; Department of Computer Engineering; College of Engineering; 366984Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the a posteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between the Transformer classifiers theoretically analyzed in this paper and the ones used in practice today is illustrated by means of classification problems in natural language processing.Publication Metadata only Peer-to-peer multipoint video conferencing with layered video(Academic Press Ltd-Elsevier Science Ltd, 2011) Akkuş, İstemi Ekin; Civanlar, Mehmet Reha; Department of Computer Engineering; Özkasap, Öznur; Faculty Member; Department of Computer Engineering; College of Engineering; 113507A peer-to-peer (P2P) architecture for multipoint video conferencing using layered video coding at the end hosts is proposed. The system primarily targets end points with low bandwidth network connections and enables them to create a multipoint video conference without any additional networking and computing resources beyond what is needed for a point-to-point conference. For P2P multipoint video conferencing applications, wide-area collaboration is significant for connecting participants from different parts around the globe to support collaborative work. In our system, peers collaborate for streaming video, and the motivation behind the use of layered video is to overcome the problem of denying video requests by peers and assure that each participant peer can view any other participant at any configuration. Layered video encoding techniques usable within this architecture are discussed. A protocol for operating the system has been developed, simulated and its performance has been analyzed. Furthermore, a multi-objective optimization approach has been developed to simultaneously minimize the number of base layer receivers and the delay experienced by the peers while maximizing the granted additional requests to support peers having multiple video input bandwidths. The use of the multi-objective optimization scheme is demonstrated through an example scenario and simulations. A prototype has also been implemented, and the system has been formally specified and verified.Publication Metadata only A second-order adaptive network model for organizational learning and usage of mental models for a team of match officials(2022) Kuilboer, Sam; Sieraad, Wesley; van Ments, Laila; Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/AThis paper describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organizational learning in team-related performances. The paper describes the value of using shared mental models to illustrate the concept of organizational learning, and factors that influence team performances by using the analogy of a team of match officials during a game of football and show their behavior in a simulation of the shared mental model. The paper discusses potential elaborations of the different studied concepts, as well as implications of the paper in the domain of teamwork and team performance, and in terms of organizational learning.Publication Metadata only Hot spots in protein-protein interfaces: towards drug discovery(Elsevier, 2014) N/A; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Çukuroğlu, Engin; Engin, Hatice Billur; Gürsoy, Attila; Keskin, Özlem; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; N/A; College of Engineering; College of Engineering; N/A; N/A; 8745; 26605Identification of drug-like small molecules that alter protein-protein interactions might be a key step in drug discovery. However, it is very challenging to find such molecules that target interface regions in protein complexes. Recent findings indicate that such molecules usually target specifically energetically favored residues (hot spots) in protein protein interfaces. These residues contribute to the stability of protein-protein complexes. Computational prediction of hot spots on bound and unbound structures might be useful to find druggable sites on target interfaces. We review the recent advances in computational hot spot prediction methods in the first part of the review and then provide examples on how hot spots might be crucial in drug design. (C) 2014 Published by Elsevier Ltd.Publication Metadata only Structured adaptive mesh refinement adaptations to retain performance portability with increasing heterogeneity(IEEE Computer Society, 2021) Dubey, Anshu; Berzins, Martin; Burstedde, Carsten; Norman, Michael L.; Wahib, Mohammed; Department of Computer Engineering; Erten, Didem Unat; Faculty Member; Department of Computer Engineering; College of Engineering; 219274Adaptive mesh refinement (AMR) is an important method that enables many mesh-based applications to run at effectively higher resolution within limited computing resources by allowing high resolution only where really needed. This advantage comes at a cost, however: greater complexity in the mesh management machinery and challenges with load distribution. With the current trend of increasing heterogeneity in hardware architecture, AMR presents an orthogonal axis of complexity. The usual techniques, such as asynchronous communication and hierarchy management for parallelism and memory that are necessary to obtain reasonable performance are very challenging to reason about with AMR. Different groups working with AMR are bringing different approaches to this challenge. Here, we examine the design choices of several AMR codes and also the degree to which demands placed on them by their users influence these choices.Publication Metadata only Challenges and applications of automated extraction of socio-political events from text (case 2021): workshop and shared task report(Association for Computational Linguistics (ACL), 2021) Tanev, Hristo; Zavarella, Vanni; Piskorski, Jakub; Yeniterzi, Reyyan; Villavicencio, Aline; Department of Sociology; Department of Sociology; N/A; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Mutlu, Osman; Yüret, Deniz; Teaching Faculty; Faculty Member; PhD Student; Faculty Member; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; N/A; 28982; N/A; 179996This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.Publication Metadata only EASER: energy aware scalable and reactive replication protocol for MANETs(Springer, 2016) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Azar, Saeed Nourizadeh; Karaağaçlı, Kaan; Özkasap, Öznur; PhD Student; Undergraduate Student; Faculty Member; Department of Computer Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 113507Mobile ad hoc networks (MANETs) depend on the nodes' collaboration to communicate and transfer data, and scaling the network size up greatly increases the energy needed to transfer data among far away nodes. To preserve nodes' energy and increase the network lifetime, data replication protocols have been proposed, which mainly increase data availability by creating nearby local copies of required data. In this work, first we provide a review of energy aware data replication protocols in MANETs. Then, by considering nodes' energy consumption, we propose EASER: Energy Aware Scalable and rEactive data Replication protocol. Our simulation results and comparison with SCALAR, energy aware ZRP and AODV protocols show that EASER provides improved network lifetime and data accessibility as the network size scales up with considering node energy levels.Publication Metadata only Special section on the 2011 joint symposium on computational aesthetics (CAe), non-photorealistic animation and rendering (NPAR), and sketch-based interfaces and modeling (SBIM)(Pergamon-Elsevier Science Ltd, 2012) Isenberg, Tobias; Asente, Paul; Collomosse, John; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632N/APublication Metadata only Per-GOP bitrate adaptation for 11.264 compressed video sequences(Springer, 2006) De Martin, J.C.; Department of Computer Engineering; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; De Vito, Fabio; Özçelebi, Tanır; Civanlar, Mehmet Reha; Tekalp, Ahmet Murat; Other; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 16372; 26207In video transmission over packet data networks, it may be desirable to adapt the coding rate according to bandwidth availability. Classical approaches to rate adaptation are bitstream switching, requiring the storage of several pre-coded versions of a video, or layered (scalable) video coding, which has coding efficiency and/or complexity penalties. In this paper we propose a new GOP-level rate adaptation scheme for a single stream, variable target bitrate H.264 encoder; this allows each group of pictures (GOP) to be encoded at a specified bitrate. We first compare the performance of the standard H.264 rate control algorithm with the proposed one in the case of constant target bitrate. Then, we present results on how close the new technique can track a specified per-GOP target bitrate schedule. Results show that the proposed approach can obtain the desired target rates with less than 5% error.Publication Metadata only Combining protein-protein interaction networks with structures(Cell Press, 2009) Nussinov, Ruth; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Kar, Gözde; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 26605