Greece

Twitter is generating a large amount of real-time data in the form of microblogs that has potential knowledge for various applications like traffic incident analysis and urban planning. Social media data represents the unbiased actual insights of citizens concerns that
may be mined in making cities smarter. In this study, a computational framework has been proposed using word embedding and machine learning model to detect traffic incidents using social media data. The study includes the feasibility of using machine learning algorithms with different feature extraction and representation models for the identification of traffic incidents from the Twitter interactions. The comprehensive proposed approach is the combination of following four steps. In the first phase, a dictionary of traffic-related keywords is formed. Secondly, realtime Twitter data has been collected using the dictionary of identified traffic related keywords. In the third step, collected tweets have been pre-processed, and the feature generation model is applied to convert
the dataset eligible for a machine learning classifier. Further, a machine
learning model is trained and tested to identify the tweets containing traffic incidents. The results of the study show that machine learning
models built on top of right feature extraction strategy is very promising to identify the tweets containing traffic incidents from micro-blogs.

AIAI 2020

Harnessing Social Interactions on Twitter for Smart Transportation Using Machine Learning

traffic incident detection

twitter data analysis

machine learning

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

We present acoustic resonance testing on glass intravenous (IV) bottles. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. 
Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods. 
The results from the binary classification experiments are presented and compared in terms of Balanced Accuracy Rate, F1-score, Area Under the Receiver Operating Characteristic Curve, and Matthews Correlation Coefficient metrics.
The presented feature analysis and the employed classifiers achieved solid results, despite the rather small and imbalanced dataset with a highly inconsistent class population.

Acoustic Resonance Testing of Glass IV Bottles

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Au- tomated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interest- ing predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.

Chemical Laboratories 4.0: A Two-stage Machine Learning System for Predicting the Arrival of Samples

Surfing is currently one of the most popular water sports in the world, both for recreational and competitive level surfers. Surf
session analysis is often performed with commercially available devices.
However, most of them seem insufcient considering the surfers' needs,
by displaying a low number of features, being inaccurate, invasive or not adequate for all surfer levels. Despite the fact that performing manoeuvres is the ultimate goal of surng, there are no available solutions that enable the identication and characterization of such events. In this work, we propose a novel method to detect manoeuvre events during wave riding periods resorting solely to the inertial sensors embedded in smartphones. The proposed method was able to correctly identify over
95% of all the manoeuvres in the dataset (172 annotated manoeuvres), while achieving a precision of up to 80%, using a session-independent validation approach. These ndings demonstrate the suitability and validity
of the proposed solution for identication of manoeuvre events in real-world conditions, evidencing a high market potential.

Real-time Surf Manoeuvres' Detection Using Smartphones' Inertial Sensors

Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior change, due to increased attacker's sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. To detect intensive attacks and increase device uptime, we propose a novel ensemble learning model for IoT anomaly detection using software-dened networks (SDN). We use a deep auto-encoder to
extract handy features for stacking into an ensemble learning model. The learned model is deployed in the SDN controller to detect anomalies or dynamic attacks in IoT by addressing the class imbalance problem. We validate the model with real-time testbed and benchmark datasets. The initial results show that our model has a better and more reliable performance than the competing models showcased in the relevant related work.

SDN-enabled IoT Anomaly Detection using Ensemble Learning

This paper presents a novel Machine Learning (ML) approach to support the creation of woven fabrics. Using data from a textile company, two CRoss-Industry Standard Process for Data Mining (CRISP-DM) iterations were executed, aiming to compare three input feature representation strategies related with fabric design and finishing processes. During the modeling stage of CRISP-DM, an Automated ML (AutoML) procedure was used to select the best regression model among six distinct state-of-the-art ML algorithms. A total of nine textile physical properties were modeled (e.g., abrasion, elasticity, pilling). Overall, the simpler yarn representation strategy obtained better predictive results. Moreover, for eight fabric properties (e.g., elasticity, pilling), the addition of finishing features improved the quality of the predictions. The best ML models obtained low predictive errors (from 2% to 7%) and are potentially valuable for the textile company, since they can be used to reduce the number of production attempts (saving time and costs).

Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques.

Overlap-based Undersampling Method for Classification of Imbalanced Medical Datasets

Long term stress often causes depression and neuronal atrophies that in turn can lead to a variety of health problems. As a result of these cellular changes, also molecular changes occur. These changes, that include increase of glucocorticoids and decrease of the brain-derived neurotrophic factor, have the unfortunate effect that they decrease the cognitive abilities needed for the individual to solve the stressful situation. Such cognitive abilities like reappraisal and their adaptation mechanisms turn out to be substantially impaired while they are needed for regulation of the negative emotions. However, antidepressant treatments and some other therapies have proved to be quite effective for the strengthening of such cognitive abilities. This study introduces an adaptive causal network model for this phenomenon where a subject loses his or her cognitive abilities (negative metaplasticity) due to long-term stress and re-improve these cognitive abilities (positive metaplasticity) through mindfulness-based cognitive therapy (MBCT). Simulation results have been reported for demonstration of the phenomenon.

Know Yourself: An Adaptive Causal Network Model for Therapeutic Intervention for Regaining Cognitive Control

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs' clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

Bridging the Gap between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

Cancer, which involves the dysregulation of genes via multiple mechanisms, is unlikely to be fully explained by a single data type. By combining different "omes", researchers can increase the discovery of novel biomolecular associations with disease-related phenotypes. Investigation of functional relations among genes associated with the same disease condition may further help to develop more accurate disease relevant prediction models. In this work, we present an integrative framework called Data & Analytic Integrator (DAI), to explore the relationship between different omics via different mathematical formulations and algorithms. In particular, we investigate the combinatorial use of molecular knowledge identified from omics integration methods netDx, iDRW and SSL, by fusing the derived aggregated similarity matrices and by exploiting these in a semi-supervised learner. The analysis workflows were applied to real-life data for ovarian can-cer and underlined the benefits of joint data and analytic integration.

Multi-omics data and analytics integration in ovarian cancer

The use of chatbots evolved rapidly in numerous fields in recent years. In this presentation, we first present a historical overview of the evolution of the international community's interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots' usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chat-bots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation.

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