Greece

With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. In this paper, we examine the problem of classifying hotel critiques using views expressed in users&#39; reviews. There is a massive development of opinions and reviews on the web, which invariably include assessments of products and services, and beliefs about events and persons. In this study, we aim to face the problem of the forever increasing amount of opinionated data that is published in a variety of data sources. The intuition is the extraction of meaningful services despite the lack of sufficient existing architectures. Another important aspect that needs to be taken into consideration when dealing with brand monitoring, relates to the rapid heterogeneous data processing, which is vital to be implemented in real-time in order for the business to react in a more immediate way.

AIAI 2020

A Sentiment-based Hotel Review Summarization using Machine Learning Techniques

opinion mining

machine learning

sentiment analysis

neural networks

classification

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. In this paper, we examine the problem of classifying hotel critiques using views expressed in users' reviews. There is a massive development of opinions and reviews on the web, which invariably include assessments of products and services, and beliefs about events and persons. In this study, we aim to face the problem of the forever increasing amount of opinionated data that is published in a variety of data sources. The intuition is the extraction of meaningful services despite the lack of sufficient existing architectures. Another important aspect that needs to be taken into consideration when dealing with brand monitoring, relates to the rapid heterogeneous data processing, which is vital to be implemented in real-time in order for the business to react in a more immediate way.

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.

Transformation is not only today's trend but also a reality.
Ports could not be excluded from that change. A transformation pro-
cess has been initiated in order to change their operational structure,
and the services they oer. Articial Intelligent and Data oriented ser-
vices push the services' landscape beyond the traditional ones that are
currently used. The scope of this paper is to analyze and scrutinize the
opportunities that are risen for Telecommunications / Information and
Communication Technology (ICT) providers at ports. These opportuni-
ties are the stepping stone towards the transformation of ports for the
future. This work in progress is under the DataPorts project that is
funded by the European Union's Horizon 2020 Research and Innovation
Programme under grant agreement No. 871493.

Towards a smart port: the role of the telecom industry

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.

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

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

The data science and AI community have gathered around the world to support tackling the climate change problem in different domains. This research aims to work on air quality through emissions and pollutant concentration data along with vegetation information. Authorities, especially in urban cities like London, have been very vigilant in monitoring these different aspects of air quality and reliable sources of big data is available in this domain. This study aims to mine and collate this information spread all over the place in different formats into a usable knowledge base on which further data analysis and powerful Machine Learning approaches can be built to extract strong evidence useful in building better policies around climate change.

A Sentiment-based Hotel Review Summarization using Machine Learning Techniques

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