Greece

This paper addresses the subject of Movie Recommendation Systems, focusing on two of the most well-known filtering techniques, Collaborative Filtering and Content-based Filtering. The first approach proposes a supervised probabilistic Bayesian model that forms recommendations based on the previous evaluations of other movies the user has watched. The second approach composes an unsupervised learning technique that forms clusters of users, using the K-Means algorithm, based on their preference of different movie genres, as it is expressed through their ratings. Both of the above approaches are compared to each other as well as to a basic method known as Weighted Sum, which makes predictions based on the cosine similarity and the euclidean distance between users and movies. In addition, Content-based Filtering is implemented through K-Means clustering techniques that focus on identifying the resemblance between movie plots. The first approach clusters movies according to the Tf/Idf weighting scheme, applying weights to the terms of movie plots. The latter identifies the likeness between movie plots, utilizing the BM25 algorithm. The efficiency of the above methods is calculated through the Accuracy metric.

AIAI 2020

Improving Movie Recommendation Systems Filtering by Exploiting User-based Reviews and Movie Synopses

content-based filtering

movie recommendation systems

recommendation systems

collaborative filtering

text analysis

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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 purpose of this paper is the expansion of the set-based model, namely an information retrieval model, with the use of graphs. The indexing process implements a graphical representation, while the querying and document representation are based on the classical set-based model. The root of the set-based model corresponds to the use of term sets to complete the querying process based on the terms of the query. However, in the weighting process, this paper presents a wholly different approach elaborating on algorithms that may clearly benefit the process based on the k-core decomposition of each single graph. The main focus will finally be the estimation and presentation of the most important nodes belonging to each graph. These intend to be regarded as keywords presenting the evaluation of their major influence.

A Graph-based Extension for the Set-based Model Implementing Algorithms based on Important Nodes

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.

A Sentiment-based Hotel Review Summarization using Machine Learning Techniques

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.

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