Category: Events

20th of April, at 13h00, Antônio Fröhlich will give a presentation entitled“SmartData: a Data-centric Approach to the Design of Safety-Critical Systems” 
Location: G4.1

Bio
Antônio Augusto Fröhlich is a full professor at the Federal University of Santa Catarina (UFSC), where he leads the Software and Hardware Integration Laboratory (LISHA) since 2001. He holds a PhD in Computer Engineering from the Technical University of Berlin (2001) and was a visiting researcher at the University of Paderborn (2007), at the University of California, Irvine (2016) and at the University of Luxembourg (2017). He has coordinated several research and innovation projects in connected, secure and intelligent embedded systems, including the ALTATV Digital TV Platform, the CIA² research network on Smart Cities and the Internet of Things, the Smart Campus project at UFSC and Smart Grid projects with partner industries. In 2022, he was a founding member of UFSC’s Research Center for Cyber-physical Systems Security (SecCPS), currently acting as Executive Director.

Abstract
Data is at the core of the design of modern Safety-Critical Systems. Data is no longer only sensed and processed in the context of the control loops of such systems. It is also secured, stored, and transmitted for the sake of the decision-making processes required for higher levels of autonomy. The task-centered strategies traditionally used to design critical systems consistently support scheduling analysis and verification of tasks execution times, as long as periods, deadlines and execution time estimates are known, but mostly ignore the flow of data across the various components in the system and often assume that data generation time is constant and can be fully encapsulate in the execution time of tasks. A Data-driven approach to the design of such systems can more promptly accommodate requirements such as data freshness, redundant data sources, operational safety, and AI-readiness. It also facilitates the design of mechanisms to monitor, and eventually override, non-deterministic components such as the neural networks commonly used in autonomous systems. In this talk, we present the SmartData concept, which was conceived at LISHA/UFSC to handle these design issues. The talk addresses aspects of domain decomposition, scheduling, communication, formal property specification and verification, and component overriding. The Autonomous Vehicle being built at UFSC lays a case background for the talk.

14th of April, at 14h00, Alexandru Iosup will give a presentation entitled“Massivizing Graph Processing: The Science, Design, and Engineering of a Complex Ecosystem” 
Location: A5.4

Bio
Dr.ir. Alexandru Iosup is a full professor at Vrije Universiteit Amsterdam (VU), a high-quality research university in the Netherlands. He is the tenured chair of the Massivizing Computer Systems research group at the VU and visiting researcher at TU Delft. He is also elected chair of the SPEC-RG Cloud Group. His work in distributed systems and ecosystems includes over 150 peer-reviewed articles with high scientific impact, and has applications in cloud computing, big data, scientific and business-critical computing, and online gaming. His research has received prestigious recognition, including membership in the (Young) Royal Academy of Arts and Sciences of the Netherlands, the Netherlands ICT Researcher of the Year award, and a PhD from TU Delft. His leadership and innovation in education led to various awards, including the prestigious Netherlands Higher-Education Teacher of the Year. He has received a knighthood for cultural and scientific merits. Contact Alexandru at A.Iosup@vu.nl or @AIosup, or visit http://atlarge.science/aiosup

Abstract
Wherever we turn, our society is digital. Digital data and digitalized processes are becoming critical for science and engineering, decision-making and business-critical operations, and online education and gaming. An emerging building block of digitalization in the 21st century, graph representation of both real and digital states, is becoming common in practice. At societal and even global scale, these digital elements depend, often transparently from the perspective of their clients, on the effective inter-operation of efficient computer systems into large ecosystems, managed largely without a developer and even client input. However successful until now, we cannot take these ecosystems for granted: the core does not rely on sound principles of science and design, and there are warning signs about the scalability, dependability, and sustainability of engineering operations. This is the challenge of massivizing computer systems, and, as the focus here, of massivizing graph processing. 
In this talk, inspired by the recently started EU project Graph Massivizer [1], and by our experience with distributed computer systems for over 15 years, we focus on understanding, deploying, scaling, and evolving graph-processing ecosystems successfully. We explain GraphMassivizer’s ambitious, comprehensive research program that aims to develop a serverless, sustainable, graph processing platform, with applications to sustainable ICT infrastructure, AI/ML, FinTech, and Industry 4.0. We posit that we can address the fundamental challenges of massivizing graph processing by focusing on computer ecosystems rather than merely on (individual, small-scale) computer systems. We showcase diverse results in massivizing graph processing, including (1) a reference architecture, and a resource management and scheduling framework, that can span the computing continuum, (2) the design and development of graph processing engines, and (3) the development of a benchmark and performance analysis framework that led to a fundamental performance result. 
The session will be interactive, so bring over your questions, comments, and curiosity. We will have a team with diverse professional backgrounds ready to answer them. Thank you for joining!
[1] Graph Massivizer website: https://graph-massivizer.eu/

12th of March at 16h00, Anamta Khan and Jessica Castro  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

Anamta Khan – “A Machine Learning driven Fault Tolerance Mechanism for UAVs’ Flight Controller” 

Bio
Anamta Khan is a PhD student in the SSE group at DEI. She obtained her Master’s and Bachelor’s degrees in Computer Science from the Institute of Business Administration (IBA) in Karachi. Her research focus since 2021 has been on creating a safer airspace with maximum and efficient utilization of drones. She utilizes fault injection in a simulation-based environment to identify vulnerabilities and their impact, and then uses machine learning-based techniques to address them.

Abstract
Unmanned Aerial Vehicles (UAVs), similar to other robotic systems, are susceptible to various hazards (e.g., software or hardware failures or security attacks) that may hinder mission completion and compromise safety by violating the separation minima (i.e., the minimum distance that must be maintained between UAVs in order to ensure safe and efficient operations). To address this issue, this paper proposes a new machine learning-based fault-tolerant mechanism for UAV flight controllers that tolerates GPS-related hazards and improves safety. Machine learning models were built using 884,410 data records from 1,985 flight logs collected from the PX4 public repository. The trained models are used to predict the expected position of the UAV during a mission, and separation minima are used as a threshold to detect the GPS hazards by comparing it with the distance between two consecutive position values. When a hazard is detected (i.e., the distance is higher than separation minima), the predicted values by machine learning models are fed into the position estimator (i.e., EKF) of the flight controller. To evaluate the effectiveness of this approach, validation experiments were conducted on several realistically defined missions while being exposed to different types of failure conditions (e.g., Noise and GPS failure), both with and without using the proposed fault-tolerant mechanism. The results show a remarkable reduction in the safety violations from 94 to 1 (violation of the separation minima counts), indicating a significant improvement in safety. Additionally, the proposed mechanism demonstrated a notable improvement in the distance covered and duration of the flight mission in failure conditions, demonstrating its ability to mitigate faults effectively. These findings support the effectiveness of the proposed fault tolerance mechanism in enhancing UAV mission performance by improving safety and tolerating issues caused by GPS.

Jessica Castro – “Attack Detection in Microservice Applications” 

Bio
Jessica Castro is a PhD candidate in Informatics Engineering at the University of Coimbra, Portugal. She received a BSc. in Computer Science and an MSc in Informatics from Universidade Federal da Paraíba, Brazil. Her research interests include attack detection, microservices/containerised applications, and self-adaptive systems.

Abstract
A microservices-based architecture decreases the complexity of developing new systems, making them highly scalable and manageable. However, its distributed nature, the high granularity of services, and the large attack surface increase the challenge of securing those systems. We will present the challenges of monitoring and identifying attacks in microservice applications at runtime and how we aim to use logic scoring of preference to calculate scores that allow identifying possible attacks.

29th of March, at 16h00, Gabriel Campos will give a presentation entitled“Towards safe autonomous driving” 
Location: G4.1  (speaker will be remote)

Bio
Gabriel R. Campos is a Technical Expert on Precautionary Safety and Research Manager at Zenseact, where he works on safe planning and decision making for ADAS and AD systems. With a background on control theory, his research interests include safety assurance, behavioural prediction and threat-assessment and decision-making techniques. He has driven and participated to several production and research projects on robotic and autonomous vehicles topics, with a particular emphasis on safety critical systems and collision avoidance techniques. He received his Ph.D. in Automatic Control in 2012 from Grenoble University/Grenoble INP, France. Prior to joining the Zenseact, he was a visiting researcher at KTH, Sweden, as well as a postdoctoral fellow with the Department of Signals and Systems, Chalmers University of Technology, Sweden and the DEIB, Politecnico di Milano, Italy.

Abstract
This talk will focus on Zenseact’s journey towards safe automation solutions. We will provide an overview of our technology and  development platform, and describe some of our concepts and visions regarding the development of autonomous vehicles. We will also provide an overview of our research activities and bring forward some challenges and future research avenues.

8th of March at 16h00, Gonçalo Carvalho and José Pereira 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

Gonçalo Carvalho – “From the ER+ conceptual model to its logical model” 
Bio
Gonçalo Carvalho has a background in Geography and after a change of field is currently doing his Ph.D. research in Data models for multi-layer systems. His major research interests are in the areas of databases, distributed systems, edge computing, cyber-physical systems, and green computing.

Abstract
Distributed databases and data transformation mechanisms are remarkably relevant for Business Intelligence and Data Analytics. The Entity-Relational (ER) model is fundamental for modeling complex enterprise systems, but has shortcomings. ER+ tackled the representation of multiple database locations and conceptually expressed data transportation and data transformation operations, such as aggregate and line functions, which are standard for data analytics. The new ER+ concepts need a logical representation, which we will introduce in this talk. 

José Pereira – “On the Use of Deep Graph CNN to Detect Vulnerable C Functions and Function Prioritization Techniques” 
Bio
José D’Abruzzo Pereira is a Ph.D. student in Informatics Engineering at the University of Coimbra (UC) and a member of the Software and System Engineering (SSE) group at CISUC. His research interests include security and vulnerability detection, static code analysis, software project management, software quality, and self-adaptive systems. He received a MSc in Information Technology and Software Engineering from the University of Coimbra and Carnegie Mellon University and a BSc. in Computer Science from the State University of Campinas – Brazil (Unicamp). He is also acting as a professor in the Specialization in Software Engineering at the State University of Campinas – Brazil (Unicamp) and as an Invited Assistant Professor at the University of Coimbra.

Abstract
Software vulnerabilities are a problem in most software systems. If left unchecked, they can be exploited by malicious third parties to compromise the system, which can result in hazardous consequences. Over the years, several techniques have been proposed to tackle the problem of automatically detecting vulnerabilities. However, despite the efforts, they usually issue many false alarms, which create a large overhead for the development team to analyze them. In this work, we study the viability of using a static technique (developed initially to classify classes of malware) to detect vulnerable C functions. This technique uses the Control Flow Graph (CFG) of the functions, features related to the structure of the graph, and the code sequence. Different from the malware classification problem, we also extract memory management-related features. A Deep Graph Convolutional Neural Network (DGCNN) processes all of the features. To do that, we use vulnerable and non-vulnerable functions of the open-source Linux Kernel project. Results show that a high recall can be obtained using this approach at the cost of low precision. At this point, a new prioritization mechanism is under development, and it uses Quality Models (QMs) to rank the functions. In addition, a security expert classification will help validate the prioritization mechanism.

22nd of February, at 16h00, Henrique Madeira will give a presentation entitled“How neuroscience and artificial intelligence are radically changing the software engineering field: two examples of new tools” 
Location: G4.1

Bio
Henrique Madeira is full professor at the University of Coimbra, where he has been involved in the research on dependable computing since 1989. His main research interests focus on experimental evaluation of dependable computing systems, including security evaluation and benchmarking, fault injection techniques and error detection mechanisms. His recent research projects involve two research directions: a) Assured AI, focusing on providing safety and security guaranties in critical applications that use AI and b) human factors in software engineering, particularly on the use of biometrics to improve software quality. He has coordinated or participated in dozens of projects and was the Vice-Chair of the IFIP Working Group 10.4 Special Interest Group (SIG) on Dependability Benchmarking. Henrique Madeira has served as Head of the Department of Informatics Engineering at University of Coimbra from 2002-2004, President of the Centre for Informatics and Systems of University of Coimbra from 2006-2008, and Head of the Scientific Council of the Department of Informatics Engineering at University of Coimbra from 2005-2006. Henrique Madeira was a founding member of the spin-off company Critical Software SA.

Abstract
Software development is an intellectually demanding task. The high complexity of software, particularly code complexity, is traditionally considered the main contributing factor to software reliability issues. Complex code is hard to test, difficult to comprehend by programmers, and hence difficult to maintain. The result is that software defects (i.e., bugs) persist a the most enduring and hard to solve problem of the software industry. Unreliable software represents a huge cost for the society.
Since software bugs are the result of human errors, a recent research trend has emerged using neuroscience to identify the brain mechanisms involved in software error making/discovery, and the correlated psychophysiological manifestations that can be captured by wearable and non-intrusive devices (e.g., bracelets and smart watches). This has the potential to create a radically new neuroscience-enabled technology to assist software developers with the identification of conditions that may cause programmers making software faults or bugs escaping to human attention. Although this research line is relatively new, the number of works in recent year has exploded.
This talk addresses the recent advances in this interdisciplinary area and describes some of the most recent experimental results and corresponding neuroscience-enabled tools that can help software programmers to reduce the number of bugs in software products.