Category: Events

13th of September, at 16h00, Katerina Goseva-Popstojanova will give a presentation entitled“Reliable and Secure Cyberspace” 
Location: G4.1

Bio
Dr. Katerina Goseva-Popstojanova is a Professor at the Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA. Her research interests are in the areas of software engineering, cybersecurity, applied data analytics, and higher education in these areas. She received the National Science Foundation (NSF) CAREER award in 2005 and has served as a Principal Investigator on various NSF, NASA, Department of Defense (DoD), and industry funded projects. She is serving as an Academic Coordinator of the M.S. in Software Engineering program and leading the B.S. in Cybersecurity program at West Virginia University. She serves as an Associate Editor of the IEEE Transactions on Reliability and was a Program co-Chair of ISSRE 2007 and QRS 2021, and a General co-Chair of ISSRE 2022. 

Abstract
Today’s society heavily relies on dependable operation of software, systems, and networks. The research activities of the Reliable and Secure Cyberspace (RelSeC) Lab at West Virginia University seek to develop experimental, analytical, and AI techniques for development, assurance, and quantitative assessment of reliable and secure software and systems. This talk will address several recent RelSeC research efforts, such as characterization of software vulnerabilities, automatic identification of security-related bug reports, software defect prediction, malware detection using multimodal machine learning, and empirical analysis of bugs in software developed using model-based software engineering and in software with autonomous functionality. 

12th of July at 16h00, Omid Asghari and Fernando Vidal  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

Omid Asghari – “Lead Time Analysis for UAVs failure prediction in U-space”

Bio
Omid is a second-year Ph.D. student at the University of Coimbra. He earned his Bachelor’s degree in Computer Engineering – Software from the University of Kurdistan in 2014. Subsequently, he decided to immigrate to Tehran to pursue a master’s degree at the Islamic Azad University, Science and Research branch in Tehran, which he completed in 2018. During his master’s program, he gained practical experience in the industry as a software developer, specializing later as an application security specialist, accumulating a total of six years of professional experience. Omid’s research interests primarily focus on the safety and security of Unmanned Aerial Vehicles (UAVs) and U-space services, secure coding, and the dependability and security of computer systems.

Abstract
In recent years, UAVs have been increasingly used in urban environments due to agility in movement, simplicity in me- chanics, low price, and ability to access locations that are difficult or impossible to reach by humans. A significant number of drones are expected to fly in the urban sky shortly. The profitable nature of commercial UAVs/drone applications in urban space will imply a high density of drones; therefore, avoiding mid-air collisions will be critical for the safe operation of the UAVs. In Europe, U- space services are being created to guarantee the safe operations of UAVs in urban Very Low Level (VLL) airspace. In order to avoid collisions, U-space considers a separation minima (i.e., the minimum safe distance between UAVs) surrounding each UAV. Thus, violating the separation minima, which might be caused by abnormal conditions (e.g., bad weather conditions), failure conditions (e.g., GPS failure in UAVs), or unreliable behavior of the system (e.g., inaccurate GPS positioning data or erratic position estimation by flight controller), could potentially result in conflicts that require immediate mitigation measures to avoid mid-air collisions. Failure prediction is a promising method for preventing separation minima violations in U-space services. However, in order to have effective failure prediction, the lead time, which is the time between the activation of a fault and its manifestation in a system as a failure, must account for both the prediction step and the subsequent mitigation actions. This paper aims to evaluate the lead time in UAV systems in the presence of positioning-related issues (as being critical for the safe operation of UAVs) from a U-space perspective. We used fault injection to inject 18 different types of faults (or emulating failures) in 28 different UAV missions. The results show that the lead time for 17 types of faults injected is at least 14 seconds (in some cases, no failure occurred). Thus, U-space has at least 14 seconds to predict and mitigate such faults. In the case of GPS failure (i.e., GPS signal is entirely missing), lead time is about 5 seconds, requiring faster strategies for failure prediction and mitigation plans.

Fernando Vidal – “OpenSCV: An Open Hierarchical Taxonomy for Smart Contract Vulnerabilities”

Bio
Fernando Vidal is a Ph.D. student at the University of Coimbra, Portugal. His research interests are related to blockchain technology. Fernando has been publishing at international conferences, addressing some of his findings of blockchain technology, such as vulnerabilities in smart contracts and revocation. Fernando has applied his acquired knowledge, helping companies implement blockchain technology through consulting.

Abstract
Smart contracts are nowadays at the core of most blockchain systems, as they specify and allow an agreement between entities that wish to perform a transaction. As any computer program, smart contracts are subject to the presence of residual faults, including severe security vulnerabilities, which require that the vulnerable contract is terminated in the blockchain. In this context, research began to be developed to prevent the deployment of smart contract holding vulnerabilities, mostly in the form of vulnerability detection tools. Along with these efforts, several and heterogeneous vulnerability classification schemes arised (e.g., most notably DASP and SWC). At the time of writing, these are mostly outdated initiatives, despite the fact that smart contract vulnerabilities are continuously being discovered and the associated rich information being mostly disregarded. In this paper, we propose OpenSCV, a new and Open hierarchical taxonomy for Smart Contract Vulnerabilities, which is open to community contributions and matches the current state of the practice, while being prepared to handle future modifications and evolution. The taxonomy was built based on the analysis of research on vulnerability classification, community-maintained classification schemes, and research on smart contract vulnerability detection. We show how OpenSCV covers the announced detection ability of current vulnerability detection tools, and highlight its usefulness as a resource in smart contract vulnerability research.

11th of July, at 15h00, Goran Mauša will give a presentation entitled“Efficient evolutionary computing” 
Location: G4.1

Bio
Goran Mauša obtained his PhD in computer science on the topic of software defect prediction at the University of Zagreb in 2016. His current position is assistant professor at the Faculty of Engineering, University of Rijeka, and head of Department of computer engineering. His research interests include machine learning and soft computing applied to the field of peptide chemistry, environmental engineering, and sustainability. He is currently involved in 8 scientific projects, participated in the organization of 9 conferences and 7 PhD forums, authored 14 journal papers and 35 conference papers and posters.

Abstract
In computer science, evolutionary computing is a family of nature-inspired algorithms for solving complex search-based and optimization problems. The idea of evolutionary computing is to find the best solution to a given problem in a smaller number of steps than traditional and computationally demanding approaches like exhaustive or grid search. Our aim is to investigate whether this strategy can be made even greener through hyper-parameter tuning, selecting the appropriate optimization algorithm and programming language, building surrogate models and neuroevolution.

23rd of June, at 14h00, Karthik Pattabiraman will give a presentation entitled“Building Error Resilient Machine Learning Systems from Unreliable Components” 
Location: G4.1

Bio
Karthik Pattabiraman is a Professor of Electrical and Computer Engineering at the University of British Columbia (UBC). He received his MS and PhD in computer science from the University of Illinois at Urbana Champaign (UIUC) in 2004 and 2009, and spent a postdoctoral year at Microsoft Research (MSR), Redmond before joining UBC in 2010. His research interests are in dependability, security, and software engineering. Karthik has won multiple awards such as the Inaugural Rising Star in Dependability Award, 2020, from the IEEE and the IFIP, the distinguished alumnus award from the University of Illinois (UIUC), CS department, 2018, and multiple UBC-wide awards for excellence in research and mentoring. Together with his students and collaborators, he has published over 100 papers, many of which have received distinguished paper awards at venues such as DSN and ICSE. He is a distinguished contributor of the IEEE computer society, a distinguished member of the ACM, and the vice-chair of the IFIP Working Group on dependable computing and fault-tolerance (WG 10.4).  A more detailed biography may found at: https://blogs.ubc.ca/karthik/about/full-bio/

Abstract
Machine Learning (ML) has increasingly been adopted in safety-critical systems such as Autonomous vehicles (AVs) and industrial robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. Hardware faults such as soft errors are becoming more frequent in commodity computer systems due to the effects of technology scaling and reduced supply voltages. These faults can lead to ML systems malfunctioning, and cause safety violations. Further, errors in the  training data have been widely observed even in mature  training datasets, and these can lead to significant degradation of accuracy in ML algorithms. Therefore, there is a compelling need to protect ML systems from both hardware faults and training data errors. 
In this talk, I’ll present  some of the work we’re doing in my group to ensure the dependability of ML systems in the presence of hardware faults and training data errors. For the former, we introduce Ranger, an automated transformation for Deep Neural Network (DNN)-based systems that can filter out the hardware faults that are likely to have the most impact in the DNN. For the latter, I’ll present the use of ensemble-based techniques, and show that they outperform most other techniques proposed in the ML community for dealing with training data errors.  This is joint work with my students and colleagues at UBC, as well as with industry collaborators.  

14th of June at 16h00, Sandino B. Jardim and Gustavo Callou  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

Sandino B. Jardim – “Evolving Benchmarking Methodologies for SDN Controllers” 

Bio
Sandino B. Jardim is a professor at the Federal University of Mato Grosso (UFMT). He holds an M.Sc and a Ph.D. in Computer Science from the Federal University of Goiás (UFG) and the Federal University of Rio Grande do Norte (UFRN), respectively. Currently, he is conducting postdoctoral research at CISUC in the field of benchmarking methodologies for SDN Controllers.

Abstract
The emergence of Software-Defined Networking (SDN) brought about a paradigm shift in the networking landscape. SDN controllers quickly emerged as potential systems under test (SUT) for benchmarking, with most works focusing on traditional system quality attributes such as performance and scalability. As SDN evolved as a key enabler for subsequent innovations like network function virtualization, IaaS, IoT, and 5G, its role expanded and gained significant importance for the success of these technologies. Benchmarking methodologies also transformed, incorporating additional quality attributes such as dependability and reliability. Given the shifting landscape of the SDN domain and the growing need for comprehensive benchmarking, there is a demand for research on updating benchmarking methodologies for SDN controllers. This research aims to address the evolving requirements and advancements in SDN technology, ensuring that benchmarking practices keep pace with emerging challenges and opportunities.

Gustavo Callou – “An Approach to Integrate Performance Assessment, Dependability, Energy Efficiency, and Security Applied to Cloud Computing” 

Bio
Gustavo Callou is an associate professor at the Federal Rural University of Pernambuco (UFRPE), Brazil. He has a Ph.D. in Computer Science from the Federal University of Pernambuco (UFPE), Brazil, with a split-site doctoral program at University of Wuppertal, Germany, in Dependability and Performance Evaluation. Callou is the leader of the Performance and Data Analysis Lab (PEDAL) at UFRPE, and his key research interests include Reliability Analysis, Fault-Tolerant Computing, Performance Engineering, Sustainability, and Cloud Computing.

Abstract
Energy efficiency represents an important factor that can be used to reduce the environmental impact. Forecasting required resources in conjunction with an energy optimization strategy represents a challenging problem because of cloud computing environments’ dynamic nature and varying workload characteristics. In the literature, dynamic migration of virtual machines (VMs) between servers is commonly proposed to increase security, although it has essential disadvantages, such as migration cost and performance degradation. This project aims to propose an approach to assist in managing resources in virtualized private clouds, and that seeks to optimize the number of servers to meet the requirements of dynamic workloads. This research will also seek to quantify the impact of security policies (e.g., VM migration) on performance, power consumption, and dependability. To evaluate this proposed strategy, experiments will be conducted in the laboratory with a private cloud environment and models will be proposed to assist in optimization and identify the best approach to deal with these conflicting requirements.

24th of May, at 16h00, Chriss IT. Leong will give a presentation entitled“Translating Natural Language Requirements to Formal Specifications: A Study on GPT and Symbolic NLP” 
Location: G4.1

Bio
Chriss IT. Leong is a Chief Information Security Officer, EHR, Department of Information Security, Health Bureau. Is is also a Phd Student at CISUC, Department of Informatics Engineering, University of Coimbra. He has a Master and Bachelor in Computer Science from National Yang Ming Chiao Tung University, Taiwan. His research interests focus on Software Engineering, Formal Specification and Software Verification.

Abstract
Software verification is essential to ensure dependability and that a system or component fulfils its specified requirements. Natural language is the most common way of specifying requirements, although many verification techniques such as theorem proving depend upon requirements being written in formal specification languages. Automatically translating requirements into a formal specification language is a relevant and challenging research question, because developers often lack the necessary expertise. We consider the application of natural language processing (NLP) to address such research question. Two distinct approaches are proposed to formalise natural language requirements: a symbolic method and a GPT-based method. Both methods are evaluated with respect to their ability to generate accurate Java Modeling Language (JML) from textual requirements, and the results show good promise for automatic formalisation of requirements.

17th of May, at 16h00, Zoran Budimac will give a presentation on the research conducted at the Department of Mathematics and Informatics of the University of Novi Sad
Location: G4.1

Bio
Zoran Budimac was born in 1960. in Serbia. The academic rank title of full professor got from University of Novi Sad in 2004. Currently works at Faculy of Sciences, Univesity of Novi Sad, Serbia.
During his career he has published 280 research papers, 16 books and presented his work on 180 international conferences.Currently, he is the head of one laboratory and the head of chair of computer scince. he is also  a member of University counsil and member of management committee of an international journal.
Currently, his scientific interest are in various aspects of software quality.