Probabilistic model for the analysis of elastic session duration in wireless networks

In wireless networks, the bitrate received by a user session depends not only on the fraction of allocated resources of the base station but also on the number of factors affecting the signal-to-noise ratio. These factors include a distance between a mobile device and the base station, walls, buildings between them, etc. As a result, the bitrate of elastic sessions may differ tenfold. In this paper, we develop a probabilistic model that captures the main specifics of the data transmission through a wireless network by introducing the random serving rate coefficients concept. With the help of the model, we derived the cumulative distribution function of the elastic session duration.

Web multimedia files characterization

his thesis introduces multimedia workload characterization embedded in popular Web pages and serving by YouTube. The findings of the study are used to build a multimedia workload generator. The workload generator can be used in simulations that investigates methods in improving caching performance for serving multimedia files, reducing multimedia network traffic, increasing YouTube scalability, decreasing startup delay when playing audio/video files embedded in a Web page or serving by short video sharing services, and evaluating the performance of multimedia servers. In this research, first, an analysis on Web pages consisting of multimedia embedded objects is presented. Also, a characterization study of around 250,000 YouTube popular and regular videos is performed. Based on the analysis of popular Web pages and measurement of YouTube traffic, a workload generator is developed. The workload generator generates the files of popular Web pages and YouTube servers and simulates a user session when accessing a server.

Web multimedia files characterization

his thesis introduces multimedia workload characterization embedded in popular Web pages and serving by YouTube. The findings of the study are used to build a multimedia workload generator. The workload generator can be used in simulations that investigates methods in improving caching performance for serving multimedia files, reducing multimedia network traffic, increasing YouTube scalability, decreasing startup delay when playing audio/video files embedded in a Web page or serving by short video sharing services, and evaluating the performance of multimedia servers. In this research, first, an analysis on Web pages consisting of multimedia embedded objects is presented. Also, a characterization study of around 250,000 YouTube popular and regular videos is performed. Based on the analysis of popular Web pages and measurement of YouTube traffic, a workload generator is developed. The workload generator generates the files of popular Web pages and YouTube servers and simulates a user session when accessing a server.

Facilitating Test Status Information Communication in the Time of COVID-19

The Coronavirus 2019 pandemic has strained nearly every aspect of pathology practice, including preanalytic, analytic, and postanalytic processes. Much of the challenges result from high demand for limited severe acute respiratory syndrome coronavirus 2 testing capacity, a resource required to facilitate patient flow throughout the hospital system and society at large. At our institution, this led to unprecedented increases in inquiries from providers to laboratory staff relating to the expected time to result for their patients. The demand was great enough to require redeployment of staff to handle the laboratory call volume. Although these data are available in our laboratory information system, the data do not interface to our electronic health record system. We developed systems using the R statistical programming language that abstract the necessary data regarding severe acute respiratory syndrome coronavirus 2 polymerase chain reaction testing from our lab system in real time, store it, and present it to clinicians for on demand querying. These data have been accessed over 2500 times by over 100 distinct users. Median length of each user session is approximately 4.9 minutes. Because our lab information system does not persistently store tracking information while our system does, we have been able to iteratively recalculate time to result values for each tracking stop as workflows have changed over time. Facility with informatics and programming concepts coupled with clinical understanding have allowed us to swiftly develop and iterate on applications which provide efficiency gains, allowing laboratory resources to focus on generating test results for our patients.

INFRASTRUCTURE MODEL FOR A SECURE VIRTUAL ENVIRONMENT FOR INFORMATION INTERACTION BETWEEN SCIENTIFIC AND EDUCATIONAL ORGANIZATION

The article describes the construction of a basic display model in the information space of the organizational and technical association of subjects of information interaction in the implementation of joint projects and solving common problems. Some aspects of the construction of a set-theoretic model of the infrastructure of a secure virtual environment (SVE) of information interaction of scientific and educational organizations are considered. The basis of the infrastructure level of the SVE is a network of corporate portals, for which the model describes the relationship between network entities and rules of information interaction. The key element of information interaction is the portal, which is considered as a set of interconnected resources (access objects) that have a hierarchical tree structure of subordination. Multiple portal users (access subjects) are united through domain groups in a network structure that implements mechanisms of trust between domain groups, as well as one-time authorization and a single entry point to the portal network by implementing a single user session. The basic model presented in the article is the basis of a generalized model of the protected virtual environment of information interaction in general.

Exploring temporal contexts for neighborhood-based models in Session-Based Recommender Systems

Recent works in the field of Recommender Systems have focused in making recommendations using only information about accessed items in an active user session, since the user’s preferences can be very specific to the context of the session. Despite the increasing interest in deep neural network models in the Recommender Systems in general, simpler models, such as neighborhood-based collaborative filtering ones, have been outperforming these more complex models in the session-based scenario. However, in order to soften known scalability problems in neighborhood-based models, sampling strategies during the recommendation process have been proposed, showing that some session contexts such as recency of neighbor sessions can improve the recommendation in some domains. Therefore this project explores the effect of different temporal contexts that can easily be captured from an anonymous user session, such as the hour of the day and day of the week, as alternatives to the established sampling methods.

Learning pathway: Content Management and Identification of Student Behavior Through Online Learning Management in Java

learning path in the online learning system refers to a series of objects which are structured to help students in improving their knowledge in a particular subject. Content is provided to the students and a proper path is set so there will not be any distraction or confusion while studying. In conventional way of teaching if the students would miss one session they will not be able to understand the next session and students will lose interest in the subject, the chances of failure will be more. By providing the content, students can understand the flow so they won’t miss any topic and understand the subject. The session is provided so that the students can be tracked and they wouldn’t miss any topic and score well. Admin will manage the content by adding or removing the content according to the technology and monitor the session time, teachers will get the information of students by admin and teachers will track the students, students will study the content. It is useful to identify the behaviour of the student through the tracking of the user session. Based on the time spent on the content the behavioural pattern of the students is predicted and analysed. The user is analysed based on the behavioural patterns like time spent on the content, skipping of content and the session time. It is hypothesized that the students will be able to get knowledge deeper and can master the subject. It is a web-based java tutoring system which provides an online platform for students. It provides appropriate content for students to learn and understand the concepts clearly.