STEC: See-Through Transformer-based Encoder for CTR Prediction

Click-Through Rate (CTR) prediction holds a pivotal place in online advertising and recommender systems since CTR prediction performance directly influences the overall satisfaction of the users and the revenue generated by companies. Even so, CTR prediction is still an active area of research since it involves accurately modelling the preferences of users based on sparse and high-dimensional features where the higher-order interactions of multiple features can lead to different outcomes. Most CTR prediction models have relied on a single fusion and interaction learning strategy. The few CTR prediction models that have utilized multiple interaction modelling strategies have treated each interaction to be self-contained. In this paper, we propose a novel model named STEC that reaps the benefits of multiple interaction learning approaches in a single unified architecture. Additionally, our model introduces residual connections from different orders of interactions which boosts the performance by allowing lower level interactions to directly affect the predictions. Through extensive experiments on four real-world datasets, we demonstrate that STEC outperforms existing state-of-the-art approaches for CTR prediction thanks to its greater expressive capabilities

DETERMİNATİON OF VOLATİLE COMPOUNDS OF THE FIRST ROSE OİL AND THE FİRST ROSE WATER BY HS-SPME/GC/MS TECHNİQUES

Background: Rose water and rose oil are used in the perfume, cosmetic, pharmaceutical and food industries. The determination of volatile compounds in rose oil and rose water obtained from oil-bearing rose is highly important in terms of availability in the industry and in human health. Materials and Methods: Thus, in this study, volatile compounds in the first rose oil and first rose water have been determined by HS- SPME/GC/MS (Headspace-Solid Phase Micro Extraction/Gas Chromatography Mass Spectrometry) techniques which were not published previously for determining volatile compounds in rose oil and rose water. Twenty four and twenty six volatile compounds were determined in the first rose oil and in the first rose water, respectively. Results: It was further discovered that both first rose oil and first rose water are rich in oxygenated monoterpenes and sesquiterpenes, with a third group of volatile compounds known as aliphatic hydrocarbons being found only in first rose oil. Citronellol contents of the first rose oil and rose water were found to be 43.40% and 40.13% respectively, whereas geraniol contents were 11.81% and 15.97%, respectively. Conclusion: These findings suggest that HS-SPME/GC/MS is a suitable technique for the determination of volatile compounds of rose oil and rose water

The Ninth Visual Object Tracking VOT2021 Challenge Results

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The Seventh Visual Object Tracking VOT2019 Challenge Results

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering &amp; Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia</p