Greece

Recent developments of data monitoring and analytics technologies in the context of wireless networks will boost the capacity to extract knowledge about the net-work and the users. On the one hand, the obtained knowledge can be useful for running more efficient network management tasks related to network reconfigura-tion and optimization. On the other hand, the extraction of knowledge related to user needs, user mobility patterns and user habits and interests can also be useful to provide a more personalized service to the clients. Focusing on user mobility, this paper presents a methodology that predicts the future Access Point (AP) that the user will be connected to in a Wi-Fi Network. The prediction is based on the historical data related to the previous APs which the user connected to. Different approaches are proposed, according to the data that is used for prediction, in or-der to capture weekly, daily and hourly user activity-based behaviours. Two pre-diction algorithms are compared, based on Neural Networks (NN) and Random Forest (RF). The methodology has been evaluated in a large Wi-Fi network de-ployed in a University Campus.

AIAI 2020

On the Prediction of Future User Connections Based on Historical Records in Wireless Networks

length of stay prediction

wi-fi network

supervised learning

The 27th International Conference on Systems, Signals and Image Processing.

IWSSIP 2020

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.

This presentation, like the paper, tries to shed new light on the overall definition of the threat landscape that emerges by the convergence of cloud computing and IoT in a smart grid

Threat Landscape of Next Generation IoT-enabled Smart-Grids

The YAKSHA project aims at reinforcing EU-ASEAN cooperation and building partnerships in cybersecurity domain by developing a solution tailored to specific national needs leveraging EU know-how and local knowledge. YAKSHA enhances cybersecurity readiness levels for its end users, helps better prevent cyber-attacks, reduces cyber-risks and better governs the whole cybersecurity process. The EU-ASEAN cooperation also helps to mitigate some of the weaknesses identified in the cybersecurity ecosystem. In this paper, we consider the protocol deployment process for employing honeypot-as-a-service, with focus on the Internet of Things (IoT) use case.

Protocol Deployment for Employing Honeypot-as-a-Service

This paper presents a concept of integration of MEC into the 5G network slicing architecture. Three variants of the architecture have been proposed, which incorporate: individual MEP/MEPM for each slice, shared ones for multiple network slices and Distributed Autonomous Slice Management and Orchestration (DASMO) approach. Each variant is focused on efficient integration of 5G Core and MEC solutions and utilizing additional functionalities of both components, including MEC APIs and 5G Control Plane exposure capabilities. Finally, main issues of 5G-MEC implementation have been discussed, which include the aspects of MEC service APIs, MEC Apps mobility in demanding use cases, challenges of service continuity in roaming scenarios as well as the role and availability of 5G enablers.

A new approach to 5G and MEC integration

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.

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

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.

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