Greece

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.

AIAI 2020

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

deep learning· cnn· lstm· bilstm· cryptocurrency price prediction· time series

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.

technical paper

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.

Deepbots: A Webots-based Deep Reinforcement Learning Framework for Robotics

Neural Architecture Search is becoming an increasingly popular research field and method to design deep learning architectures. Most research focuses on searching for small blocks of deep learning operations, or micro-search. This method yields satisfactory results but demands prior knowledge of the macro architecture's structure. Generally, methods that do not utilize macro structure knowledge perform worse but are able to be applied to datasets of completely new domains. In this paper, we propose a macro NAS methodology which utilizes concepts of Regularized Evolution and Macro Neural Architecture Search (DeepNEAT), and apply it to the Fashion-MNIST dataset. By utilizing our method, we are able to produce networks that outperform other macro NAS methods on the dataset, when the same post-search inference methods are used. Furthermore, we are able to achieve 94.46% test accuracy, while requiring considerably less epochs to fully train our network.

Regularized Evolution for Macro Neural Architecture Search

The recent advancements in the areas of computer vision and deep learning with the development of convolutional neural networks and the profusion of highly accurate general purpose pre-trained models, create new opportunities for the interaction of humans with systems and facilitate the development of advanced features for all types of platforms and applications. Research, consumer and industrial applications increasingly integrate deep learning frameworks into their operational flow, and thanks to the availability of high performance hardware (Computer Boards, GPUs, TPUs) also for individual consumers and home use, this functionality has been moved closer to the end-users, at the edge of the network. In this work, we exploit the aforementioned approaches and tools for the development of an edge-native platform for exergames, which includes innovative gameplay and features for the users. A prototype game was created using the platform that was deployed in the real-world scenario of a rehabilitation center. The proposed approach provides advanced user experience based on the automated, real-time pose and gesture detection, and in parallel maintains a low cost to enable wide adoption in multiple applications across domains and usage scenarios.

Introducing an edge-native deep learning platform for exergames

The increase of the adoption of IoT devices and the contemporary problem of food production have given rise to numerous applications of IoT in agriculture. These applications typically comprise a set of sensors that are installed in open fields and measure metrics, such as temperature or humidity, which are used for irrigation control systems. Though useful, most contemporary systems have high installation and maintenance costs, and they do not offer automated control or, if they do, they are usually not interpretable, and thus cannot be trusted for such critical applications. In this work, we design Vital, a system that incorporates a set of low-cost sensors, a robust data store, and most importantly an explainable AI decision support system. Our system outputs a fuzzy rule-base, which is interpretable and allows fully automating the irrigation of the fields. Upon evaluating Vital in two pilot cases, we conclude that it can be effective for monitoring open-field installations.

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

In this presentation, we investigate a special case of an open problem that is related to the exchange principle, a property of fuzzy implications. We focus on the cases of (S,N)- implications and the preservation of the exchange principle via lattice operations. We present and prove some sufficient conditions such that the exchange principle is preserved under the join and meet operations if we use (S,N)-implications.

Preservation of the Exchange Principle via Lattice Operations on (S,N)-Implications

In fuzzy hypothesis testing we use
fuzzy test statistics produced by fuzzy estimators and fuzzy critical values.
In this paper we use the non-asymptotic fuzzy estimators in fuzzy hypothesis testing. These are triangular shaped fuzzy numbers that generalize the fuzzy estimators based on confidence intervals in such a way that eliminates discontinuities and ensures compact support.
Our approach is particularly useful in critical situations, where
subtle fuzzy comparisons between almost equal statistical quantities have to be made. In such cases the hypotheses tests that use non-asymptotic fuzzy estimators give better results than the previous approaches, since they give us the possibility of partial rejection or not of the null hypothesis.

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