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Redundant encoding and packaging for segmented live media
No full textWe present the MPEG standardization activity on redundant encoding and packaging for live segmented media. The standardization includes profiling the Dynamic Adaptive Streaming over HTTP (DASH) Media Presentation Description for ingest, storage and redundant packaging applications. Further, a Common Media Application Format (CMAF) segment and track format is defined to support redundant encoding and packaging using a common timeline relative to the Unix epoch. The standardization is still ongoing and we solicit feedback from academic and industry practitioners
En iyi çözümlü karar ağaçları için kayarak ileriyi gören yaklaşımlar
No full textClassification trees have gained tremendous attention in machine learning applications due to their inherently interpretable nature. Current state-of-the-art formulations for learning optimal binary classification trees suffer from scalability for larger depths or larger instances. Moreover, they mostly fail to prove optimality after long run times and fit perfectly to the training data while minimizing misclassification error which is likely fail to generalize to the test data. We present a simple but powerful new formulation which we call rolling look-ahead learning approach. By dropping tractability variables which are dependent on instance size, we present a novel two-depth optimal binary classification tree formulation with the objective to minimize gini impurity or misclassification error. The approach can be thought of as a middle ground between myopic and global optimization methods. For larger depths, we developed a hybrid approach which learns by looking ahead 2-steps rolling horizon. It is much faster than the fastest known global optimization methods which can solve an instance with around 50K rows & 135 features in less than 4 minutes, for depth 8. Also, in majority of cases, the proposed approach outperforms global optimization methods & CART in terms of win count tested for 7 depths, 10 Fold and 19 benchmark datasets, and increase in out-of-sample accuracy up to 16.8% and 11.9% with respect to global optimization methods and CART, respectively.Son zamanlardaki ikili sınıflandırma karar ağacı ̈oğrenim en iyileme formülasyonları, büyük derinliklerde veya büyük verisetlerinde ̈olçeklenebilirlikle ilgili sorunlar yaşamaktadır. Uzun saatler süren ̧calışma sürelerine rağmen, en iyi değeri kanıtlamakta sorun yaşamaktadırlar. Ayrıca, eğitim sürecinde amaç fonksiyonu olarak yanlış sınıflandırma metriği sonucu, test setinde yanlı bir sonuç ̧cıkabilmekte, iyi sınıflandırma yapmakta sorun yaşamaktadır. Bu ̧calışmada, ̈onceki ̧calışmaların veri boyutuna bağlı olan ve takip eden değişkenlerinden kurtulup, CART gibi algoritmalardan da esinlenerek, 2 derinlikli ağaçlar için yenilikçi bir formülasyon sunuyoruz. Algoritma açgözlü yaklaşımlar ve global optimizasyon yöntemleri arasında kalan spektrumdan faydalanıyor. Daha büyük derinlikli ağaçlar için ise, 2 seviye ileri görerek öğrenen hibrit bir algoritma geliştirdik. Bu algoritma, yaklaşık 50 bin satırlı, 135 ̈ozellikli bir veride 8 derinlikli bir ağacı 4 dakikadan daha kısa sürede çözebilir ve gelişime açıktır. Ayrıca, mevcutta bulunan en iyi global optimizasyon ve CART yaklaşımını skor sayısıyla geçebilirken, global optimizasyon modellerine göre test setinde %16.8'e kadar bir artış, CART'a göre ise %11.9'a kadar bir artış gözlemlenmiştir. Gözlemler 19 veri seti, 7 farklı derinlik ve 10 katlamalı veri grubunda test edilmiştir.
How organizational dehumanization impacts hospitality employees service recovery performance and sabotage behaviors: the role of psychological well-being and tenure
No full textPurpose: The purpose of this study is to examine the mediating effect of psychological well-being between organizational dehumanization and two outcome variables: service recovery performance and service sabotage. This research also investigates whether organizational tenure moderates the relationship between organizational dehumanization and psychological well-being. Design/methodology/approach: Using survey methodology, 200 hotel frontline service employees (FLEs) in Turkey were sampled over two time points. Additionally, employees’ direct supervisors rated their service recovery performance. The partial least squares method, specifically SmartPLS 3.3.3, was used for data analysis. Findings: The results indicate that organizational dehumanization negatively influences employees’ psychological well-being. However, organizational tenure moderates this relationship, in which organizational dehumanization has less of a negative effect on employees’ psychological well-being in those with longer tenure. Psychological well-being was found to mediate the relationship between organizational dehumanization and service recovery performance. Finally, psychological well-being mediates the relationship between organizational dehumanization and service sabotage. Practical implications: Managers should consider the negative effect organizational dehumanization has on FLEs’ psychological well-being and aim to establish an organizational culture that values these employees as individuals and as invaluable resources for the organization. Further, this study has found that less tenured employees are less likely to have the psychological resources to cope with organizational dehumanization and are more susceptible to decreased productivity (i.e. service recovery performance) and engaging in counterproductive work behaviors (i.e. service sabotage) due to mistreatment in the workplace. Originality/value: This study furthers our understanding of organizational dehumanization, an understudied concept in hospitality research, which influences employee outcomes. The findings of this study contribute to the advancement of the self-determination theory and how organizational dehumanization impacts psychological well-being. It also contributes to the conservation of resources theory and current literature on service recovery performance and service sabotage
Genetic algorithms and heuristics hybridized for software architecture recovery
No full textLarge scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.TÜBİTA
Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery
No full textAutonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images
Modernizing data and cloud practices: A review and model proposal for future studies
No full textThe interaction of society is transforming towards connectivity and digital experiences. This results in big data which cannot be utilized and managed by companies in an efficient way. Despite various applications of the cloud and other advanced technologies, companies cannot distinguish essential information adequately. Consequently, there is a need for data modernization, but there are only partial modernization solutions and no incorporative model. Thus, a literature review was leveraged to isolate 15 related criteria, which are then grouped under 3 main dimensions (i) business, (ii) organizational and cultural, and (iii) technical, which then delivers a generic model suitable to guide and measure the transformation of companies.Özyeǧin Universit
BERT2OME: Prediction of 2′-O-methylation modifications from RNA sequence by transformer architecture based on BERT
No full textRecent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method B2O to infer 2'-O-methylation RNA modification sites from RNA sequences. B2O combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, B2O assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were and respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that B2O reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods. Our code and datasets can be found at
Dynamic CDN switching - dash-if content steering in dash.js
No full textThis paper overviews the content steering specification currently being developed in DASH Industry Forum and first implemented in the dash.js reference player
Anthropologist Lloyd A. Fallers’ Research in Turkey during the 1960s
No full textDue to the influence of the modernization paradigm, as the main axis of the American social sciences during the Cold War, and since the 1950s, the field of anthropology has shown interest in the developing nation-states in addition to primitive societies. Within this context, Turkey was one of the ‘new nations’ to become a potential object of analysis for Western anthropologists as Turkey was already praised as an exemplary democracy in the political science literature. As a result, for the first time in the history of American anthropology, a series of ethnographies pertaining to Turkey were generated in the 1960s. This article brings to light the academic portrait of the anthropology professor Lloyd A. Fallers at Chicago University and his studies on Turkey. Fallers worked on Turkey for more than ten years, did long-term fieldwork during his residences in Turkey for a total of two years, and supervised dissertations about Turkey. Fallers’ work, which has mostly remained unpublished due to his early death, is analyzed by relying on the archives deposited in the Chicago University library and on oral history interviews conducted by the author with people who had known Fallers.Science Academy in TurkeyPublisher versio
Studying children's object interaction in virtual reality: A manipulative gesture taxonomy for VR hand tracking
No full textIn this paper, we propose a taxonomy for the classification of children's gestural input elicited from spatial puzzle play in VR hand tracking. The taxonomy builds on the existing manipulative gesture taxonomy in human-computer interaction, and offers two main analytical categories; Goal-directed actions and Hand kinematics as complementary dimensions for analysing gestural input. Based on our study with eight children (aged between 7-14), we report the qualitative results for describing the categories for analysis and quantitative results for their frequency in occurring in children's interaction with the objects during the spatial task. This taxonomy is an initial step towards capturing the complexity of manipulative gestures in relation to mental rotation actions, and helps designers and developers to understand and study children's gestures as an input for object interaction as well as an indicator for spatial thinking strategies in VR hand tracking systems.ALLEA ; Bilim Akademisi ; International Science Counci