This paper proposes the use of a synchronized linear transform, the synchronized short-time-Fourier-transform (sSTFT), for time-frequency analysis of anechoic mixtures. We address the short comings of the commonly used time-frequency linear transform in multichannel settings, namely the classical short-time-Fourier-transform (cSTFT). We propose a series of desirable properties for the linear transform used in a multichannel source separation scenario: stationary invertibility, relative delay, relative attenuation, and finally delay invariant relative windowed-disjoint orthogonality (DIRWDO). Multisensor source separation techniques which operate in the time-frequency domain, have an inherent error unless consideration is given to the multichannel properties proposed in this paper. The sSTFT preserves these relationships for multichannel data. The crucial innovation of the sSTFT is to locally synchronize the analysis to the observations as opposed to a global clock. Improvement in separation performance can be achieved because assumed properties of the time-frequency transform are satisfied when it is appropriately synchronized. Numerical experiments show the sSTFT improves instantaneous subsample relative parameter estimation in low noise conditions and achieves good synthesis.

Privacy and verifiability are crucial security requirements in e-voting systems and combining them is considered to be a challenge given that they seem to be contradictory. On one hand, privacy means that cast votes cannot be traced to the corresponding voters. On the other hand, linkability of voters and their votes is a requirement of verifiability which has the consequence that a voter is able to check their vote in the election result. These two contradictory features can be addressed by adopting privacy-preserving cryptographic primitives, which at the same time as achieving privacy, achieve verifiability. Many end-to-end schemes that support verifiability and privacy have the need for some voter action. This makes ballot casting more complex for voters. We propose the PVPBC voting system, which is an e-voting system that preserves privacy and verifiability without affecting voter usability. The PVPBC voting system uses an effective and distributed method of authorization, which is based on revocable anonymity, by making use of a permissioned distributed ledger and smart contract. In addition, the underlying PVPBC voting system satisfies election verifiability using the Selene voting scheme. The Selene protocol is a verifiable e-voting protocol. It publishes votes in plaintext accompanied by tracking numbers. This enables voters to confirm that their votes have been captured correctly by the system. Numerical experiments support the claim that PVPBC scales well as a function of the number of voters and candidates. In particular, PVPBC’s authorization time increases linearly as a function of the population size. The average latency associated with accessing the system also increases linearly with the voter population size. The latency incurred when a valid authentication transaction is created and sent on the DLT network is 6.275 ms. Empirical results suggest that the cost in GBP for casting and storing an encrypted ballot alongside a tracker commitment is a linear function of the number of candidates, which is an attractive aspect of PVPBC.

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller’s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsivenss of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.