Category: Talk Ideas

9th of November at 16h00, Jomar Domingos and Filipa Nogueira  will give two short presentations, to promote discussion on two relevant ongoing or disruptive topics. Afterwards, there will be a social gathering where everyone can talk freely on whatever subjects they like.
Location: G4.1

Jomar Domingos – “Online Failure Prediction for cloud Applications in a Multi-level Approach” 

Bio
Jomar Domingos is a Ph.D. candidate in Informatics Engineering at the University of Coimbra, Portugal, where he also received his MSc in 2019. His current research is related to the online failure prediction for cloud applications through multi-level ensemble learning, i.e., considering cloud computing abstraction layers in the process of online failure prediction.

Abstract
Cloud computing assumes a crucial role in the current technological landscape (mainly in the internet technology industry), as it increasingly becomes the de facto approach to deploy applications and provide services through internet. Due to its complexity and heterogeneity, application failures occurrences are not uncommon, given its wide fault presence surface (from application/service to other cloud stack layers), and reactive approaches are becoming insufficient to handle failures. In some scenarios (such as business ans mission critical), dealing with failure after they happen can be very costly, being necessary to find another paradigms to deal with failures. Online failure prediction aims to deal with failures before they really happen, allowing to take measures to reduce their impact or completely avoid failures, increasing application/services dependability (improving the availability and reliability attributes).

Although previous works on this topic have already been presented, failure prediction for cloud application remains a open topic, where concrete implementations are rarely adopted and implemented. Conventional online failure prediction are focused on the prediction target, observing and modeling the its behaviour (form metric and produced logs), without considering the environment where it resides and operates (although some works considered the spatial information, it was limited to location and resource sharing information). Our focus is to investigate and explore online failure prediction for cloud applications as a multi-level problem, where prediction should be made considering every cloud abstraction layer, i.e., predict application failures on every relevant cloud platform abstraction layer.

Filipa Nogueira – “Process Mining Software Engineering Practices: A Case Study for Deployment Pipelines” 

Bio
Filipa Nogueira is a PhD candidate for the Doctoral Program in Information Science and Technology at the University of Coimbra and a member of the SSE research group. Her current main research interest focuses on the concepts related to Process Mining applied to the Software Engineering field and topics that concern improving the quality of software products and processes. She is also an Engineering Team Lead in an e-commerce company where her tasks include team and project management while ensuring software delivery with speed and quality.

Abstract
In mature software development, the deployment pipeline is the only route to deploy software into production, comprising different cycles: commit, acceptance and production. This means that DevOps teams should be able to develop, integrate and test features quickly and thoroughly via continuous practices. Even though the description of this process seems straightforward, the reality is quite different since exceptions are commonplace in actual industry practice. Process Mining provides tools to discover and check the compliance of DevOps processes while uncovering the bottlenecks and improvement areas.

This talk aims to present a case study on the adoption of Process Mining techniques in the deployment pipeline process (CI/CD) of a large European e-commerce company. The research focuses on the visibility provided by Process Discovery techniques in the DevOps workflow, namely in the process and time perspectives.

26th of October, at 16h00, Haytham Hijazi will give a presentation entitled“Cognitive Load Monitoring Through wearables: A Machine Learning Perspective” 
Location: G4.1

Bio
A Ph.D. research fellow, Centre of Informatics and Systems, University of Coimbra (CISUC), Coimbra, Portugal. Haytham Hijazi received a B.Eng. degree in Computer Systems Engineering from Palestine Polytechnic University with an excellent grade and an M.Sc. degree in Information Technology Engineering from the University of Stuttgart, Germany, in 2012 (a DAAD scholarship holder).

From 2012 to 2019, he worked with Palestine Ahliya University, Bethlehem, as a Lecturer, researcher, Data Centre Director, and Quality Assurance Manager.

Since 2019, Hijazi has been a Ph.D. Research Fellow with the Center for Informatics and Systems, University of Coimbra (CISUC). During this time, Hijazi published his work in top journals and conferences and translated one of his works into an internationally published patent application by WIPO.

Hijaz’s research interests include Explainable Machine Learning, Wearable Data Analysis, and Neuro Software Engineering (NeuroSE). His current thesis work focuses on developing intelligent biofeedback systems for augmenting content comprehension, including software engineering applications. Hijazi has broad managerial skills, including project management, academic quality assurance, and curriculum development.

Abstract
Although we are witnessing enormous growth in wearables technology (e.g., smart watches), which enables us to extract physiological measures daily, monitoring individuals’ cognitive load while performing a mental task (e.g., content comprehension) through these measures remains challenging.

Among those challenges are the inter- and intra-variability of individuals in exhibiting responses to mentally demanding tasks, models overfitting, and explainability issues.

This talk aims to introduce the results of the machine learning pipeline used in our work to predict comprehension difficulty in reading digital content using the Emaptica E4 wearable and a desktop eye-tracker. While showing the results, this talk will highlight the main challenges encountered and the measures to mitigate them.

12th of October at 16h00, Inês Valentim and Vittorio Orbinato  will give two short presentations, to promote discussion on two relevant ongoing or disruptive topics. Afterwards, there will be a social gathering where everyone can talk freely on whatever subjects they like.
Location: G4.1

Inês Valentim – “NeuroEvolution meets Adversarial Robustness” 

Bio
Inês Valentim is a Ph.D. candidate in Informatics Engineering at the University of Coimbra, Portugal, where she also received her BS and MSc in 2016 and 2019, respectively. Her current research is on the intersection of Artificial Neural Networks (ANNs), Evolutionary Computation, and Adversarial Machine Learning. In particular, she is investigating how NeuroEvolution can be leveraged to design ANNs that are more robust to adversarial examples.

Abstract
Artificial Neural Networks (ANNs) have achieved remarkable results in several domains, but their widespread adoption means that concerns other than predictive performance must be addressed. One of these concerns is their vulnerability to adversarial examples, which are carefully perturbed inputs that cause these models to produce erroneous outputs.
Manually designing and configuring ANNs becomes even more difficult under such adversarial settings. Evolution-based approaches have designed ANNs with competitive performance in the past, but the adversarial robustness of the evolved models was mostly overlooked.

In this presentation, we will overview how we plan on tackling these gaps in the literature during the Ph.D., namely by using NeuroEvolution to improve the adversarial robustness of ANNs.

Vittorio Orbinato – “Automating Adversary Emulation: a new approach to Offensive Security” 

Bio
Vittorio Orbinato is a PhD student in Information Technology and Electrical Engineering (ITEE) at Università degli Studi di Napoli Federico II, Italy.  He got his master degree at Università degli Studi di Napoli Federico II, Italy, and is currently working at the Department of Informatics Engineering (DEI), Portugal. His research interests concern Cybersecurity, Adversary Emulation and Virtualization.

Abstract
The security of software platforms and applications depends on effective techniques to detect vulnerabilities exploited by malicious attackers. To achieve this goal, the Offensive Security paradigm is becoming increasingly popular: the idea behind this approach is to test software security from an adversary perspective. Despite the advantages provided by such a paradigm, there are still many challenges related to the feasibility and costs of all the related activities.

28th of September at 16h00,  Rodrigo Ronner Tertulino da Silva will give a presentation entitled “How to ensure the privacy and security of data shared between electronic health record systems – ERH”
Location: G4.1

Bio
Rodrigo Ronner Tertulino da Silva is a professor at the Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN), Brazil. He got his master’s degree at the State University of Rio Grande do Norte (UERN), Brazil, and is working at the Department of Informatics Engineering (DEI), Portugal.He works in the following lines of research: Networks and Distributed Systems, performance evaluation of networked systems, network management. Software Engineering: Agile methods and integration with traditional approaches, object-oriented software development, including refactorings and frameworks. Security: Security in Web and PenTest applications. He is currently developing research on privacy and security in Healthcare (EHR) systems.CV (In Portuguese): http://lattes.cnpq.br/5863705420808941

Abstract
The Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN) is a public higher education institution established in 1909. Nowadays, IFRN is composed of 22 campuses strategically located in all the mesoregions of Rio Grande do Norte. It holds 40,000 students enrolled in 36 undergraduate programs, 29 graduate programs (lato sensu, masters and doctorate).
The doctoral work aims to enhance privacy issues aligned with data privacy laws and regulations in Electronic Health Record Systems (EHRs), making these systems more trustworthy for users and developers. To carry out our study and propose the development of an architectural reference for the development of EHR systems, we will also propose privacy level agreements according to essential requirements according to our research that was carried out. To evaluate our work, we will analyze systems already well known in Brazil, such as E-SUSAPS and AGHUSE, which the Brazilian government uses in public hospitals. Therefore, this doctoral work aims to propose a privacy-aware reference architecture that can guide the development of EHR systems. Security and privacy aspects are issues throughout the development cycle of these systems. Hence, allowing developers to analyze more focused aspects such as privacy and security.