Greece

The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architecture.

AIAI 2020

Network aggregation to enhance results derived from multiple analytics

network; aggregation; latent class models

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

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.

finElink is a recommendation system that provides guidance to French innovative companies with regard to their financing strategy through public funding mechanisms. Analysis of financial data from former funding recipients partially feeds the recommendation system. Financial company data from a representative French population are reduced and projected onto a two-dimensional space with Uniform Manifold Approximation and Projection, a manifold learning algorithm. Former French funding recipients' data are projected onto the two-dimensional space. Their distribution is non-uniform, with data concentrating in one region of the projection space. This region is identified using Density-based Spatial Clustering of Applications with Noise. Applicant companies which are projected within this region are labeled potential funding recipients and will be suggested the most competitive funding mechanisms.

Manifold learning for innovation funding: identification of potential funding recipients

We present a novel justification for why k-means clusters should be considered as (hyper)ball-shaped ones. We show that the clusters must be ball-shaped to attain motion-consistency. If clusters are ball-shaped, one can derive conditions under which two clusters attain the global optimum of k-means. 
We show further that if the gap is sufficient for perfect separation, then an incremental k-means is able to discover perfectly separated clusters. This is in conflict with the impression left by an earlier publication by Ackerman and Dasgupta. The proposed motion-transformations can be used to the new labeled data for clustering from existing ones.

k-means Cluster Shape Implications

The amount of devices gathering and using personal data without the person's approval is exponentially growing. The European General Data Protection Regulation (GDPR) came following the requests of individuals who felt at risk of personal privacy breaches. Consequently, privacy preservation through machine learning algorithms were designed based on cryptography, statistics, databases modeling and data mining. In this paper, we present two-levels data anonymization methods. The first level consists of anonymizing data using an unsupervised learning protocol, and the second level is anonymization by incorporating the discriminative information to test the effect of labels on the quality of the anonymized data. The results show that the proposed approaches give good results in terms of utility what preserves the trade-off between data privacy and its usefulness.

A two-levels data anonymization approach

This paper introduces a novel graph-based approach to select features from multiple textual documents. The proposed solution enables the investigation of the importance of a term into a whole corpus of documents by utilizing contemporary graph theory methods, such as community detection algorithms and node centrality measures. Compared to well-tried existing solutions, evaluation results show that the proposed approach increases the accuracy of most text classifiers employed and decreases the number of features required to achieve ‘state-of-the-art’ accuracy. Well-known datasets used for the experimentations reported in this paper include 20Newsgroups, LingSpam, Amazon Reviews and Reuters.

An innovative graph-based approach to advance feature selection from multiple textual documents

In last decade, cryptocurrency has emerged in financial area
as a key factor in businesses and financial market opportunities. Ac-
curate predictions can assist cryptocurrency investors towards right in-
vesting decisions and lead to potential increased profits. Additionally,
they can also support policy makers and financial researchers in study-
ing cryptocurrency markets behavior. Nevertheless, cryptocurrency price
prediction is considered a very challenging task, due to its chaotic and
very complex nature. In this study we evaluate some of the most success-
ful and widely used deep learning algorithms forecasting cryptocurrency
prices. The results obtained, provide significant evidence that deep learn-
ing models are not able to solve this problem efficiently and effectively.
Following the detailed problem and the conducted results analysis, we
concluded that it is essential to invent and incorporate new techniques,
strategies and alternative approaches such as: more sophisticated predic-
tion algorithms, advanced ensemble methods, feature engineering tech-
niques and other validation metrics.

Investigating the problem of cryptocurrency price prediction: A deep learning approach.

This presentation show some applications related to the use of Deep Echo State Network architecture to model and forecast heavy nonlinear processes common in the steel industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions. The results have been compared respect to another common recurrent neural network architecture called LSTM.

Deep Echo State Networks in Industrial Applications

The field of sound event detection is a growing sector which has mainly focused on the identification of sound classes from daily life situations. In most cases these sound detection models are trained on publicly available sound databases, up to now, however, they do not include acoustic data from manufacturing environments. Within manufacturing industries, acoustic data can be exploited in order to evaluate the correct execution of assembling processes. 
As an example, in this presentation the correct plugging of connectors is analyzed on the basis of multimodal contextual process information. The latter are the connector’s acoustic properties and visual information recorded in form of video files while executing connector locking processes. For the first time optical microphones are used for the acquisition and analysis of connector sound data in order to differentiate connector locking sounds from each other respectively from background noise and sound events with similar acoustic properties. Therefore, different types of feature representations as well as neural network architectures are investigated for this specific task.
The results from the proposed analysis show, that multimodal approaches clearly outperform unimodal neural network architectures for the task of connector locking validation by reaching maximal accuracy levels close to 85 %. Since in many cases there are no additional validation methods applied for the detection of correctly locked connectors in manufacturing industries, it is concluded that the proposed connector lock event detection framework is a significant improvement for the qualitative validation of plugging operations.

Using Multimodal Contextual Process Information for the Supervised Detection of Connector Lock Events

The South Asian countries have the most polluted cities in the world which has caused quite a concern in the recent years due to the detrimental effect it had on economy and on health of humans and crops. PM 2.5 in particular has been linked to cardiovascular diseases, pulmonary diseases, increased risk of lung cancer and acute respiratory infections. Higher concentration of surface ozone has been observed to have negatively impacted agricultural yield of crops. Due to its deleterious impact on human health and agriculture, air pollution cannot be brushed off as a trivial matter and measures must be taken to address the problem. Deterministic models have been actively used; but they fall short due to their complexity and inability to accurately model the problem. Deep learning models have however shown potential when it comes to modeling time series data. This article explores the use of recurrent neural networks as a framework for predicting the hazard levels in Lahore, Pakistan with 95.0% accuracy and Beijing, China with 98.95% using the time series data of air pollutants and meteorological parameters. Forecasting air quality index (AQI) and Hazard levels would help the government take appropriate steps to enact policies to reduce the pollutants and keep the citizens informed about the statistics.


Forecasting Hazard Level Of Air Pollutants Using LSTM's

A language independent deep learning (DL) architecture for machine
translation (MT) evaluation is presented. This DL architecture aims at the best
choice between two MT outputs, based on the reference translation
and the annotation score. The outputs were generated from a statistical machine
translation (SMT) system and a neural machine translation (NMT) system. The
model applied in two language pairs: English - Greek (EN-EL) and English -
Italian (EN-IT). In this paper, a variety of experiments with different parameter
configurations is presented. Moreover, linguistic features, embeddings representation and natural language processing (NLP) metrics (BLEU, METEOR,
TER, WER) were tested. The best score was achieved when the proposed model used source segments (SSE) information and the NLP metrics set. Classification accuracy has increased up to 5% (compared to previous related work) and
reached quite satisfactory results for the Kendall τ score.

Innovative Deep Neural Network Fusion for Pairwise Translation Evaluation

Deep Reinforcement Learning (DRL) is increasingly used to
train robots to perform complex and delicate tasks, while the develop-
ment of realistic simulators contributes to the acceleration of research on
DRL for robotics. However, it is still not straightforward to employ such
simulators in the typical DRL pipeline, since their steep learning curve
and the enormous amount of development required to interface with DRL
methods significantly restrict their use by researchers. To overcome these
limitations, in this work we present an open-source framework that com-
bines an established interface used by DRL researchers, the OpenAI Gym
interface, with the state-of-the-art Webots robot simulator in order to
provide a standardized way to employ DRL in various robotics scenarios.
Deepbots aims to enable researchers to easily develop DRL methods in
Webots by handling all the low-level details and reducing the required de-
velopment effort.

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