The increasing demand for mobile 360-degree video streaming drives the need for highly immersive and smooth user experiences. However, maintaining a smooth Quality of Experience (QoE) for 360-degree video is challenging due to strict network requirements, particularly on variable mobile networks. These challenges can lead to poor streaming quality and cyber/motion sickness within immersive settings. Addressing these issues requires understanding the impact of network conditions and scalable testing under realistic scenarios. For this purpose, this paper presents an empirical analysis correlating real-world 5G radio Quality of Service (QoS) metrics from three U.S. operators with YouTube 360-degree video QoE. Our findings include a valuable dataset linking 5G radio QoS to video QoE.

Evaluating the Quality of Experience (QoE) for over-the-top (OTT) video streaming is crucial due to increasing video traffic demands for delivering satisfactory user experiences. This work presents and evaluates a rich dataset capturing real-world 5G network performance and its impact on YouTube video streaming QoE. We utilized two US-based 5G networks to stream 2D YouTube videos and monitor cellular Key Performance Indicators (KPIs) at 1-second granularity. These KPIs include Channel Level Metrics (CLMs), such as RSRP, RSRQ, and SNR, and objective QoE metrics for various scenarios, including indoor and outdoor environments with different mobility conditions. We correlate CLMs KPIs in 5G Standalone (SA) and Non-Standalone (NSA) deployments with YouTube's objective QoE. Our study shows performance differences where 5G SA encounters more streaming stalls during outdoor mobility than NSA, while SA supports higher resolutions in static indoor settings.

Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in both creating a sense of belonging for its users and evading detection. Coded language evolves quickly and its use varies over time. This paper proposes a methodology for detecting emerging coded hate-laden terminology. The methodology is tested in the context of online antisemitic discourse. The approach considers posts scraped from social media platforms, often used by extremist users. The posts are scraped using seed expressions related to previously known discourse of hatred towards Jews. The method begins by identifying the expressions most representative of each post and calculating their frequency in the whole corpus. It filters out grammatically incoherent expressions as well as previously encountered ones so as to focus on emergent well-formed terminology. This is followed by an assessment of semantic similarity to known antisemitic terminology using a fine-tuned large language model, and subsequent filtering out of the expressions that are too distant from known expressions of hatred. Emergent antisemitic expressions containing terms clearly relating to Jewish topics are then removed to return only coded expressions of hatred.

Given the dominance of adaptive video streaming services on the Internet traffic, understanding how YouTube Quality of Experience (QoE) relates to real 4G and 5G Channel Level Metrics (CLM) is of interest to not only the research community but also to Mobile Network Operators (MNOs) and content creators. In this context, we collect YouTube and CLM logs with 1-second granularity spanning a six-month period. We group the traces by their context, i.e., Mobility, Pedestrian, Bus/Railway terminals, and Static Outdoor, and derive key performance footprints of real 4G and 5G video streaming in the wild. We also develop Machine Learning (ML) classifiers to predict objective QoE video stalls by using past patterns from CLM traces. We release all datasets and software artifacts for reproducibility purposes.

The exponential increase in Dynamic Adaptive Streaming over HTTP (DASH) based video traffic with endto-end encryption poses many challenges for Mobile Network Operators (MNOs). To improve user-perceived video quality, MNOs must be aware of the end user’s Quality of Experience (QoE) by exploring network level Quality of Service (QoS). On the contrary, the network edge facility provides proximity for performing an intelligent operation to maintain smooth QoE from the network level QoS measurement. Therefore, in this work, we propose EFFECTOR, a framework to showcase lightweight in-band QoS features measurement technique at edge nodes from encrypted DASH video traffic. EFFECTOR uses an emulated environment with real 4G and 5G drive test traces to generate video traffic. Moreover, this work provides a massive dataset for analyzing the impact of 4G and 5G technology on video quality in the form of Interactive Jupyter Notebooks. The proposed framework is ideal for investigating QoS extracted from the network’s edge and finding its relations with QoE to ensure better video quality for end-users.

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.

Search engines and social networks are two entirely different data sources that can provide valuable information about Influenza. While search engine hosts can deliver popular queries (or terms) used for searching the Influenza related information, the social networks contain useful links of information sources that people have found valuable. The authors hypothesize that such data sources can provide vital first-hand information. In this article, they have proposed a methodology for detecting the information sources from social networks, particularly Twitter. The data filtering and source finding tasks are posed as classification tasks. Search engine queries are used for extracting related dataset. Results have shown that propose approach can be beneficial for extracting useful information regarding side effects, medications and to track geographical location of epidemics affected area.

In this paper we consider the optimal design and provisioning of WDM networks for the grooming of multicast subwavelength traffic. We develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming. We also introduce heuristic solutions. Optimal solutions are designed by exploiting the specifies of the problems to formulate Mixed Integer Linear Programs (MILPs). Specifically, we solve the generic multicast problem in which, given a set of multicast sessions and all destination nodes of a multicast session requiring the same amount of traffic, all demands need to be accommodated. The objective is to minimize the network cost by minimizing the number of higher layer electronic equipment and, simultaneously, minimizing the total number of wavelengths used. We also solve two interesting and practical variants of the traditional multicast problem, namely, multicasting with partial destination set reachability and multicasting with traffic thinning. For both variants, we also provide optimal as well as heuristic solutions. Also, the paper presents a number of examples based on the exact and heuristic approaches

Most of the network applications bandwidth requirements are far less than the bandwidth offered by a full wavelength in WDM networks. Hence, traffic grooming is needed to make efficient use of the available resources. In this paper we address the grooming of many-to-one traffic demands in WDM networks on arbitrary topologies. Traffic streams from different sources, but part of the same session and thus terminating at the same destination, can be aggregated using arbitrary, but application dependent, aggregation ratios. We provide optimal as well as heuristic solutions to the problem. The objective is to minimize the cost of the network, by minimizing the total number of the higher layer components and the total number of the wavelengths used in the network. One of the main contributions of this work is to provide a mixed integer linear solution, to an otherwise non-linear problem, by exploiting the specifics of routing and aggregation sub-problems, while still maintaining the optimality of the solution. The formulation is generic and can handle varying amounts of traffic from each source to a common destination, as well as arbitrary aggregation fractions of the data coming from the different sources. This fraction is made to be a function of the number of the streams participating in the aggregation. For the heuristic solution we developed a Dynamic Programming style approach that builds the solution progressively, going through a number of stages, while choosing the best partial solutions among a number of possible partial solutions at each stage

A mobile ad hoc network (MANET) can be represented by a set of logical clusters with clusterheads (CHs) acting like virtual base-stations, hence forming a wireless virtual backbone. The role of clusterhead is a temporary one, which changes dynamically as the topology or other factors affecting it change. Finding the minimal set of CHs is an NP-complete problem. We study the performance tradeoffs between two clustering approaches. The first one is a simple clustering strategy, called virtual grid architecture (VGA), which is based on a fixed rectilinear virtual topology, while the second one is an optimal clustering strategy. We consider homogeneous as well as heterogeneous networks. First, for homogeneous MANETs with a large number of users and under the VGA clustering approach, we derive expressions for the number of CHs, worst case path length, and average case path length. We also derive expressions for the communication overhead. Second, we develop an integer linear program (ILP) that finds the optimal number of connected CHs in small to medium sized heterogeneous MANETs. Analytical and simulation results show that our proposed clustering algorithm (VGA), although being simple, is close to optimal.

In This work we consider the optimal dimensioning of optical networks for multicast traffic grooming problems on WDM networks under two practical scenarios. In both cases, for each multicast session the destination set consists of two disjoint subsets. In the first scenario only one subset of each multicast session must be accommodated while the other subset can only be accommodated if this results in no additional cost. In the second case, both subsets of each multicast session must be accommodated. However, each subset has different bandwidth requirements. We develop optimal and heuristic solutions for both the cases.

We address the problem of state assignment of finite state machines (FSMs). The approach used by us to solve the state assignment problem is based on hypercube embedding. We have designed a new technique to efficiently solve the hypercube embedding problem by integrating two different techniques; one of these is the gradient projection method while the other is a variant of the Kernighan-Lin algorithm. The gradient projection method operates in continuous space and improves an initial feasible solution iteratively by tracing a search path in gradient descent direction. The Kernighan-Lin algorithm operates in discrete space and also improves an initial feasible solution iteratively. We have integrated both techniques in such a way that output from the gradient projection method is fed to the Kernighan-Lin style algorithm. The effectiveness of the proposed technique is shown by comparing its results with another technique on a number of MCNC benchmark examples for logic synthesis and optimization.