Publications without Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
Browse
27 results
Filters
Advanced Search
Filter by
Settings
Search Results
Publication Metadata only Guest editorial special issue on toward securing Internet of Connected Vehicles (IoV) from virtual vehicle hijacking(Institute of Electrical and Electronics Engineers (IEEE), 2019) Cao, Yue; Kaiwartya, Omprakash; Song, Houbing; Lloret, Jaime; Ahmad, Naveed; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211N/APublication 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 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 A classification of concurrency bugs in java benchmarks by developer intent(Association for Computing Machinery (ACM), 2006) Department of Computer Engineering; Department of Computer Engineering; N/A; Keremoğlu, M. Erkan; Taşıran, Serdar; Elmas, Tayfun; Researcher; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/AThis work addresses the issue of selecting the formal correctness criterion for a concurrent Java program that best corresponds to the developer's intent. We study a set of concurrency-related bugs detected in Java benchmarks reported in the literature. On these programs, we determine whether race-freedom, atomicity or refinement is the simplest and most appropriate criterion for program correctness. Our purpose is to demonstrate empirically the fact that the appropriate fix for a concurrency error and the selection of a program analysis tool for detecting such an error must be based on the proper expression of the designer's intent using a formal correctness criterion.Publication Metadata only Using crowdsourcing for scientific analysis of industrial tomographic images(Association for Computing Machinery (ACM), 2016) Chen, Chen; Wozniak, Pawel W.; Romanowski, Andrzej; Jaworski, Tomasz; Kucharski, Jacek; Grudzien, Krzysztof; Zhao, Shengdong; Fjeld, Morten; Department of Mechanical Engineering; Obaid, Mohammad; Undergraduate Student; Department of Mechanical Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); College of Engineering; N/AIn this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analyzed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional nonexperts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyze and understand the physics of flow phenomena to better inform the future design of silos, and is generalized enough to be applicable to other domains.Publication Metadata only Discovering black lives matter events in the United States: shared task 3, CASE 2021(Association for Computational Linguistics (ACL), 2021) Giorgi, Salvatore; Zavarella, Vanni; Tanev, Hristo; Stefanovitch, Nicolas; Hwang, Sy; Hettiarachchi, Hansi; Ranasinghe, Tharindu; Kalyan, Vivek; Tan, Paul; Tan, Shaun; Andrews, Martin; Hu, Tiancheng; Stoehr, Niklas; Re, Francesco Ignazio; Vegh, Daniel; Atzenhofer, Dennis; Curtis, Brenda; Department of Sociology; Hürriyetoğlu, Ali; Teaching Faculty; Department of Sociology; College of Social Sciences and Humanities; N/AEvaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently. But, the ability to both (1) extract events "in the wild" from text and (2) properly evaluate event detection systems has potential to support a wide variety of tasks such as monitoring the activity of socio-political movements, examining media coverage and public support of these movements, and informing policy decisions. Therefore, we study performance of the best event detection systems on detecting Black Lives Matter (BLM) events from tweets and news articles. The murder of George Floyd, an unarmed Black man, at the hands of police officers received global attention throughout the second half of 2020. Protests against police violence emerged worldwide and the BLM movement, which was once mostly regulated to the United States, was now seeing activity globally. This shared task asks participants to identify BLM related events from large unstructured data sources, using systems pretrained to extract socio-political events from text. We evaluate several metrics, assessing each system's ability to evolution of protest events both temporally and spatially. Results show that identifying daily protest counts is an easier task than classifying spatial and temporal protest trends simultaneously, with maximum performance of 0.745 (Spearman) and 0.210 (Pearson r), respectively. Additionally, all baselines and participant systems suffered from low recall (max.5.08), confirming the high impact of media sourcing in the modelling of protest movements.Publication Metadata only Cross-subject continuous emotion recognition using speech and body motion in dyadic interactions(International Speech Communication Association ( ISCA), 2017) N/A; N/A; Department of Computer Engineering; Fatima, Syeda Narjis; Erzin, Engin; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 34503Dyadic interactions encapsulate rich emotional exchange between interlocutors suggesting a multimodal, cross-speaker and cross-dimensional continuous emotion dependency. This study explores the dynamic inter-attribute emotional dependency at the cross-subject level with implications to continuous emotion recognition based on speech and body motion cues. We propose a novel two-stage Gaussian Mixture Model mapping framework for the continuous emotion recognition problem. In the first stage, we perform continuous emotion recognition (CER) of both speakers from speech and body motion modalities to estimate activation, valence and dominance (AVD) attributes. In the second stage, we improve the first stage estimates by performing CER of the selected speaker using her/his speech and body motion modalities as well as using the estimated affective attribute(s) of the other speaker. Our experimental evaluations indicate that the second stage, cross-subject continuous emotion recognition (CSCER), provides complementary information to recognize the affective state, and delivers promising improvements for the continuous emotion recognition problem.Publication Metadata only Transport protocol mechanisms for wireless networking: a review and comparative simulation study(Springer-Verlag Berlin, 2003) N/A; N/A; Department of Computer Engineering; Kanak, Alper; Özkasap, Öznur; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Increasing popularity of wireless services has triggered the need for efficient wireless transport mechanisms. TCP, being the reliable transport level protocol widely used in wired network world, was not designed with heterogeneity in mind. The problem with the adaptation of TCP to the evolving wireless settings is because of the assumption that packet loss and unusual delays are mainly caused by congestion. TCP originally assumes that packet loss is very small. on the other hand, wireless links often suffer from high bit error rates and broken connectivity due to handoffs. A range of schemes, namely end-to-end, split-connection and link-layer protocols, has been proposed to improve the performance of transport mechanisms, in particular TCP, on wireless settings. In this study, we examine these mechanisms for wireless transport, and discuss our comparative simulation results of end-to-end TCP versions (Tahoe, Reno, NewReno and SACK) in various network settings including wireless LANs and wired-cum-wireless scenarios.Publication Metadata only Type-speciric analysis of morphometry of dendrite spines of mice(Institute of Electrical and Electronics Engineers (IEEE), 2007) Fong, L.; Tasky, T. N.; Hurdal, M. K.; Beg, M. F.; Martone, M. E.; Ratnanather, J. T.; Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AIn this article, we analyze the morphometric measures of dendrite spines of mice derived from electron tomography images for different spine types based on pre-assigned categories. The morphometric measures we consider include the metric distance, volume, surface area, and length of dendrite spines of mice. The question of interest is how these morphometric measures differ by condition of mice; and how the metric distance relates to volume, surface area, length, and condition of mice. The Large Deformation Diffeomorphic Metric Mapping algorithm is the tool we use to obtain the metric distances that quantize the morphometry of binary images of dendrite spines with respect to a template spine. We demonstrate that for the values not adjusted for scale metric distances and other morphometric measures are significantly different between the conditions. The morphometric measures (rather than the mice condition) explain almost all the variation in metric distances. Since size (or scale) dominates the other variables in variation, we adjust metric distances and other morphometric measures for scale. We demonstrate that the scaled metric distances and other scaled morphometric variables still differ for condition, and scaled metric distances depend most significantly on scaled morphometric measures. The methodology used is also valid for morphometric measures of other organs or tissues and metric distances other than LDDMM.
- «
- 1 (current)
- 2
- 3
- »