Browsing Computer Science by Title
Now showing items 314-333 of 549
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Marrying WebRTC and DASH for Interactive Streaming
(ACM, 2022-03-17)WebRTC is a set of W3C and IETF standards that allows the delivery of real-time content to users, with an end-to-end latency of under half a second. Support for WebRTC is built into all modern browsers across desktop and ... -
Maximum likelihood estimate of parameters of Nakagami-m distribution
(IEEE, 2012)Nakagami-m distribution is well known for its ability to model a number of probability density functions, be it symmetric or asymmetric. Many Maximum Likelihood parameter estimation techniques for this distribution have ... -
Media over QUIC: Initial testing, findings and results
(ACM, 2023-06-08)With its advantages over TCP, QUIC created a new field for developing media-Aware low-latency delivery solutions. The problem space is being examined by the new Media over QUIC (moq) working group in the IETF. In this ... -
Medical podcasting in Iran; pilot, implementation and attitude evaluation
(Tehran University of Medical Sciences, 2013)Podcasting has become a popular means of transferring knowledge in higher education through making lecture contents available to students at their convenience. Accessing courses on media players provides students with ... -
MedSpecSearch: Medical specialty search
(Springer Nature, 2019)MedSpecSearch (www.medspecsearch.com) is a search engine for helping users to find the relevant medical specialty for a doctor visit based on users’ description of symptoms. This system is useful for users who are not sure ... -
Meta reinforcement learning for rate adaptation
(IEEE, 2023)Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions to achieve a stall-free viewing experience. Most ABR schemes use manually tuned heuristics or learning-based methods. ... -
Metadata-based user interface design for enhanced content access and viewing
(The ACM Digital Library, 2020-05)The nature of viewing is changing due to the huge volumes of content being produced including user content generated by amateurs and the proliferation of personalized services. The type of content being produced is not ... -
Metric labeling and semimetric embedding for protein annotation prediction
(Mary Ann Liebert, Inc., 2021-05-01)Computational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These techniques have been used to generate hypotheses and to direct experimental ... -
Metrics for evaluating explainable recommender systems
(Springer, 2023)Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, ... -
Minimal sign representation of boolean functions: algorithms and exact results for low dimensions
(MIT Press, 2015-08)Boolean functions (BFs) are central in many fields of engineering and mathematics, such as cryptography, circuit design, and combinatorics. Moreover, they provide a simple framework for studying neural computation mechanisms ... -
Minimizing false positive rate for DoS attack detection: A hybrid SDN-based approach
(Elsevier, 2020-06)Denial of Service attacks (DoS) are considered to be a major threat against today's communication networks. Recently, a novel networking paradigm that provides enhanced programming abilities has been proposed to attain an ... -
Mirror neurons: Functions, mechanisms and models
(Elsevier, 2013-04-12)Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were ... -
Misclassification risk and uncertainty quantification in deep classifiers
(IEEE, 2021)In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ... -
MOCMIN: convex inferring of modular low-rank contact networks over COVID diffusion data
(TÜBİTAK, 2022)SEIR (which consists of susceptible, exposed, infected, and recovered states) is a common diffusion model which could model different disease propagation dynamics across various domains such as influenza and COVID diffusion. ... -
A model for cognitively valid lifelong learning
(IEEE, 2023)In continual learning, usually a sequence of tasks are given to a learning agent and the performance of the agent after learning is measured in terms of resistance to catastrophic forgetting, efficacy of knowledge transfer ... -
Model-based runtime monitoring of smart city systems
(Elsevier, 2018)The pace of proliferation for smart systems in city wide applications is unmatched. The introduction of Internet of Things (IoT), an enabler of smart city phenomenon, has incubated a productive environment for such ... -
Model-based software product line testing by coupling feature Mmodels with hierarchical Markov chain usage models
(IEEE, 2016)Model-based testing automates test case generation based on usage models of a system. In this paper, we introduce an approach for systematic reuse of these models for testing a large family of products. In our approach, ... -
Model-based testing of digital TVs: an industry-as-laboratory approach
(Springer Science+Business Media, 2017-12)Model-based testing is a promising approach for increasing the efficiency of the testing process and for improving software quality. It has been employed in the industry for more than a decade. Nevertheless, there are still ... -
Modeling robot trust based on emergent emotion in an interactive task
(IEEE, 2021)Trust is an essential component in human-human and human-robot interactions. The factors that play potent roles in these interactions have been an attractive issue in robotics. However, the studies that aim at developing ... -
Modeling the development of infant imitation using inverse reinforcement learning
(IEEE, 2018-09)Little is known about the computational mechanisms of how imitation skills develop along with infant sensorimotor learning. In robotics, there are several well developed frameworks for imitation learning or so called ...
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