Greece

To ensure high Quality of Experience (QoE) for end users, many media applications require significantcant quantities of computing and network resources, making their realisation challenging in resource constrained environments. In this paper, we present the approach of the 5GMEDIA project, providing an integrated programmable service platform for the development, design and operations of media applications in 5G networks, facilitating media service management across the service life cycle. The platform offers tools to service developers for efficient development, testing and continuous correction of services. One step further, it provides a service virtualization platform offering horizontal services, such as a Media Service Catalogue and accounting services, as well as optimization mechanisms to flexibly adapt service operations to dynamic
conditions with ecient use of infrastructure resources. The paper outlines
three use cases where the platform was tested and validated.

AIAI 2020

Programmable Edge-to-Cloud Virtualization for 5G Media Industry: The 5G-MEDIA Approach

cognitive network optimizer

service virtualization platform 

network function virtualization

quality of experience

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

technical paper

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

The challenge of deploying mission critical services upon virtualised shared network models is the allocation of both radio and cloud resources to the critical actors who require prioritized and high-quality services. This paper describes the design and deployment of an intelligent orchestration cycle to manage end-to-end slices on a NFV architecture. This novel tool includes the monitoring of the network elements at different levels and the processing of the gathered data to produce the corresponding alert mitigation actions.

Intelligent orchestration of end-to-end network slices for the allocation of mission critical services over NFV architectures

The rapid development of network services has led to the exponential growth of online information and the increasing number of social media users. These services are exploited by malicious accounts that spread fake news and propaganda in vast user networks. Consequently, an automated solution for fake news and deception detection is required. This paper introduces a new data set consisting of 2,366 tweets written in English, regarding the Hong Kong events (August, 2019), and a well-defined method for fake news detection that uses both linguistic and network features. Our approach is tested with experiments using 2 machine learning models, achieving high performance compared to previous research.

Fake News Detection Regarding the Hong Kong Events from Tweets

Natural gas constitutes one of the most actively traded en-
ergy commodity with a significant impact on many financial activities
of the world. The accurate natural gas price prediction and the direc-
tion of price changes are considered essential since these forecasts are
utilized in energy sustainability planning, commodity trading and deci-
sion making, covering both the supply and demand side of natural gas
market. In this research, a new deep learning prediction model is pro-
posed for short-term forecasting natural gas price and movement. The
proposed forecasting model exploits the ability of convolutional layers
for providing a deep insight in natural gas data and the eficiency of
LSTM layers for learning short-term and long-term dependencies. Ad-
ditionally, a significant advantage of the proposed model is its abilities
to predict the price of natural gas on the following day (regression) and
also to predict if the price on the next day will increase, decrease or stay
stable (classfication) with respect to today's price. The conducted se-
ries of experiments demonstrated that the proposed model considerably
outperforms state-of-the-art deep learning and machine learning models.

An advanced deep learning model for short-term forecasting U.S. natural gas price and movement

As fingerprint recognition systems have become increasingly adopted within a range of technology applications over the last decade, so too has their attention within emerging research. However, although this increased attention has led to an enhancement of the software and algorithms behind this recognition pro-cess, the majority of research has still not addressed the issues of incorrect rotation or proximity between the finger and the device. Current systems assume that the direction of the imprinted finger will align with that of the target fingerprint image; this decreases the accuracy of fingerprint recognition across a variety of finger orientations and scenarios. In response to this use-case dilemma, this paper proposes a new technique of pattern matching that can account for this natural range of fingerprint orientations. This is achieved first through a preliminary stage of orientation identification, whereby the fingerprint image can be stored under multiple permutations by using approximate circular string-matching algorithms. 
This enables the database of images for each approximate permutation of orientation to be stored in advance. It can then be matched against the strong information of the fingerprint at its exact relative rotation of input. The improved accuracy of recognition demonstrated through the results of this study may enable the functionality of fingerprint recognition to adapt to challenging device form-factors and provide the accuracy needed for military and medical applications. 


Fingerprints Recognition System-Based on Mobile Device Identification using Circular String Pattern Matching Techniques

Since the discovery of motifs in molecular sequences for real genomic data, research into this phenomenon has attracted increased attention. Motifs are relatively short sequences that are biologically significant. This paper utilises the bioinformatics application of the (MMDSW) algorithm , testing it using real genomic data from large sequences. It intends to implement bioinformatics application for real genomic data, in order to discover interesting regions for all maximal motifs, in a sliding window of length ℓ, on a sequence x of length n.

Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window

EU Draft Ethics guidelines for Trustworthy AI has been proposed to promote ethical, lawful and robust AI solutions. In this article, we entertain the systemic issues and challenges of any development of the proposed guidelines.

Trustworthy AI Needs Unbiased Dictators!

The performance of solving a constraint problem can often be improved by converting a subproblem into a single constraint (for example into a regular membership constraint or a table constraint). In the past, it stood out, that specialist constraint solvers (like simplex solver or SAT solver) outperform general constraint solvers, for the problems they can handle. The disadvantage of such specialist constraint solvers is that they can handle only a small subset of problems with special limitations to the domains of the variables and/or to the allowed constraints. In this presentation we introduce the concept of fd-complete constraints and fd-complete constraint satisfaction problems, which allow combining both previous approaches. More accurately, we convert general constraint problems into problems which use only one, respectively one kind of constraint. The goal is it to interpret and solve the converted constraint problems with specialist solvers, which can solve the transformed constraint problems faster than the original solver the original constraint problems.

An Introduction of FD-Complete Constraints

Advancements in Long Short Term Memory (LSTM) Net- works have shown remarkable success in various Natural Language Gen- eration (NLG) tasks. However, generating sequence from pre-specified lexical constraints is a new, challenging and less researched area in NLG. Lexical constraints take the form of words in the language model’s out- put to create fluent and meaningful sequences. Furthermore, most of the previous approaches cater this problem by allowing the inclusion of pre- specified lexical constraints during the decoding process, which increases the decoding complexity exponentially or linearly with the number of constraints. Moreover, some of the previous approaches can only deal with single constraint. Additionally, most of the previous approaches only deal with single constraints. In this paper, we propose a novel neu- ral probabilistic architecture based on backward-forward language model and word embedding substitution method that can cater multiple lexical constraints for generating quality sequences. Experiments shows that our proposed architecture outperforms previous methods in terms of intrinsic evaluation.

Backward-Forward Sequence Generative Network for Multiple Lexical Constraints

As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems(ASR), and we benchmark three of them, namely the IBM Watson, Google, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google's automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data.

Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems

Traffic load classification for Smart Cities based on weather data collected by sensors. Comparison and evaluation of predictability for periods throughout year.

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