Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
Browse
33 results
Search Results
Publication Restricted A novel method of combining local and global data sources to estimate local impact of behavioral interventions on disease burden(Koç University, 2019) Ghanem, Angi Nazih; Ali, Özden Gür; 0000-0002-9409-4532; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 57780Publication Restricted Advantage actor-critic deep reinforcement learning approach for paint shop planning and scheduling(Koç University, 2024) Özcan, Mert Can; Türkay, Metin; 0000-0003-4769-6714; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 24956Publication Restricted Application and comparison of machine learning techniques in business(Koç University, 2021) Khuseynov, Shukhrat; Carlson, David George; 0000-0002-9736-5369; Koç University Graduate School of Sciences and Engineering; Data SciencePublication Open Access Can a smartband be used for continuous implicit authentication in real life(Institute of Electrical and Electronics Engineers (IEEE), 2020) Ekiz, Deniz; Dardağan Yağmur Ceren; Ersoy, Cem; Department of Computer Engineering; Can, Yekta Said; Department of Computer Engineering; College of EngineeringThe use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.Publication Restricted Cognitively-inspired deep learning approaches for grounded language learning(Koç University, 2021) Can, Ozan Arkan; Yüret, Deniz; 0000-0002-7039-0046; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 179996Publication Restricted Dynamic accommodation measurement using purkinje reflections and machine learning(Koç University, 2024) Özhan, Faik Ozan; Ürey, Hakan; 0000-0002-2031-7967; Koç University Graduate School of Sciences and Engineering; Electrical and Electronics Engineering; 8579Publication Restricted Efficient machine learning models for cancer biology(Koç University, 2022) Bektaş, Ayyüce Begüm; Gönen, Mehmet; 0000-0002-2483-075X; Koç University Graduate School of Sciences and Engineering; Industrial Engineering and Operations Management; 237468Publication Restricted Engaging human-robot interaction with batch reinforcement learning(Koç University, 2020) Hussain, Nusrah; Erzin, Engin; 0000-0002-2715-2368; Koç University Graduate School of Sciences and Engineering; Electrical and Electronics Engineering; 34503
Publication Metadata only Flexible-rate learned hierarchical bi-directional video compression with motion refinement and frame-level bit allocation(IEEE Computer Society, 2022) Department of Electrical and Electronics Engineering; N/A; N/A; Tekalp, Ahmet Murat; Yılmaz, Mustafa Akın; Çetin, Eren; Faculty Member; PhD Student; Undergraduate Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 26207; N/A; N/AThis paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bidirectional video compression [1] to further advance the state-of-the-art in learned video compression. As an improvement, we combine motion estimation and prediction modules and compress refined residual motion vectors for improved rate-distortion performance. As novel addition, we adapted the gain unit proposed for image compression to flexible-rate video compression in two ways: first, the gain unit enables a single encoder model to operate at multiple rate-distortion operating points; second, we exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate that we obtain state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding.Publication Restricted Generating negative samples with a related task for recommendation(Koç University, 2019) Kızıl, İpek; Akgün, Barış; 0000-0002-4079-6889; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 258784