Portrait image of Rick Kazman

Rick Kazman

Shidler College of Business
University of Hawaii at Manoa
2404 Maile Way
Honolulu HI 96822
Tel: (808) 956-6948
Fax: (808) 956-9889

The Software Technology track at HICSS is about methods, tools and techniques related to software, as distinct from the context in which it is deployed or its applications. Software Technology is among the oldest tracks at HICSS and has provided a central point of interaction among all participants in the conference, as well as a natural forum to foster new technologies. Among the topics that the Software Technology track has covered are: software engineering, security, networking, software-based product-lines, open source software, pervasive computing, artificial intelligence, agile methods, mobile/ad hoc networking, cloud computing, virtualization, parallel and distributed computing, and software assurance. The Software Technology track continues to invite novel and emerging areas of research in what remains a dynamic and exciting field.

Agile was initially designed for co-located, on-site teams, but organizations today cope with scaling issues and remote and hybrid work, which challenges the fundamental assumptions of agile and lean methods. As the agile team is becoming more diverse with the introduction of DevOps and BizDevOps, new approaches, methods, and tools and how existing methods can be tailored must be considered. How to expand the use of agile and lean beyond software development must be explored.

In this minitrack, we seek research papers and experience reports that explore practices, tools, and techniques for agile and lean development. We also seek to explore how agile concepts can be leveraged in other contexts (such as data science or engineering). Practitioners interested in submitting an experience are welcome to reach out to a minitrack co-chair for support and guidance, if desired.

Our minitrack seeks to answer questions such as:

  • How to balance team autonomy and decentralized decision-making with the need for organizational control and alignment in large-scale agile development?
  • How can we incorporate product design and development, architecture, engineering, risk reduction, budgeting and multi-shoring into agile/lean while preserving empiricism, experimentation and adaptation?
  • How can agile and lean can be integrated within a single coherent approach?
  • Which metrics help enterprises, teams and individuals adapt and improve? What common behaviors do we see in agile or lean teams and how do those behaviors affect outcomes?
  • How do organizations and cultures restructure to support these philosophies and when they do not restructure, what happens?
  • How do organizations implement, monitor and improve hiring, coaching, training and mentoring?
  • How to scale agile (how to effectively manage dependencies, teams, stakeholders, processes, technologies, and tools)including comparative results on the use of different agile scaling frameworks?
  • How can agile be implemented within other contexts (e.g., data science, BizDevOps)?
  • What organizational structures are required to enable shared leadership in self- managed teams?
  • How to balance the need for effective coordination and focused work in an agile team?
  • How do agile and lean principles extend to DevOps environments? Is there a difference between agile and lean before and after deployment? How are post- deployment issues and opportunities in software projects impacting planning and development of software development projects?

Possible additional topics for the minitrack include but are not limited to:

  • New frontiers in agile or lean management – going beyond software development
  • Forecasting, planning, testing, measurement, and metrics
  • Exploring the fit between agile (or lean) organizations and their environmental context
  • Agile and lean requirements engineering, and risk management
  • Agile in hybrid digital/physical contexts
  • What cultures, team norms and leadership characteristics lead to sustained agility?
  • Empirical studies of agile or lean organizations
  • Impact of tool use on agile or lean management
  • Education and training –new approaches to teaching and coaching agile
  • Global software development and offshoring/multi-shoring
Minitrack Co-Chairs:

Jeffrey S. Saltz (Primary Contact)
Syracuse University

Edward G. Anderson
University of Texas at Austin

Alex Sutherland
Scrum Master

Viktoria Stray
University of Oslo

The software engineering community observes that a number of software engineering tasks can be formulated as data analysis (learning) tasks and thus can be supported, for example, with AI-based methods and algorithms. This is the motivation for the research field of AI for software engineering which has developed driven by the rapid increase in size and complexity of software systems and, in consequence, of software engineering tasks.

We invite submissions of original, high-quality research papers on the topic of Artificial Intelligence for Software Engineering for our minitrack. The minitrack focuses on the practical and theoretical aspects of using artificial intelligence in software engineering practices.

Topics of interest include, but are not limited to:

  • AI-assisted software development processes and methodologies
  • AI-supported requirements engineering
  • AI-supported software architecture and design techniques
  • Automated software testing and verification using AI
  • AI for software evolution and maintenance
  • AI for program comprehension, debugging and bug fixing
  • Automated software generation using AI
  • AI-assisted reengineering and refactoring of software
  • AI-supported deployment and operating of software (AIOps)
  • Explainability and transparency in AI-assisted software engineering
  • Deployment and integration of AI technologies in software engineering processes
  • Security and privacy aspects of AI-assisted software engineering
  • User experience and usability aspects of AI-assisted software engineering
  • Tools and frameworks for AI-assisted software engineering
  • Ethical, societal, and organizational implications of AI in software engineering
  • Empirical studies and experience reports of AI in software engineering

Selected papers will be provided with a Fast Track opportunity to the a Special Issue of the Electronics Journal “Advances and Challenges in Model- and Data-based Software and Systems Engineering for Complex Systems” published by MDPI in an open Access format. (Additional publications fees will be required.)

Minitrack Co-Chairs:

Stefan Wittek (Primary Contact)
Clausthal University of Technology

Sebastian Herold
Karlstad University

Andres Rausch
Clausthal University of Technology

Emerging technologies play an increasingly important role in shaping our world and how we think, live, learn, innovate, and interact. These technologies, like artificial intelligence, blockchain, internet of things, extended reality, and more, are rapidly advancing to disrupt, transform and revolutionize our homes, workplaces, businesses, industries, governments, and societies. This minitrack explores the many factors that influence the design, development, application, adoption, use, and impact of emerging technologies. It looks at the convergence of emerging technologies and how they could address some of the most significant challenges we face in the 21st century.

We welcome theoretical, design science, case study, and field study papers from academia and practitioners that enrich our understanding of some emerging technologies individually and collectively.

Topics of interest include but are not limited to:

  • The role of emerging technologies in a sustainable world and a circular economy
  • The application, adoption, and impact of emerging technologies in digitally transforming our home, workplace, business, industry, government, and society
  • The application, adoption, and impact of emerging technologies in creating new domains, jobs, values, and possibilities
  • Case study and best practice on designing, developing, applying, adopting, using, and evaluating emerging technologies
  • Decision support models, tools, and systems with emerging technologies
  • The innovation, commercialization, and democratization of emerging technologies
  • The bright side and dark side of emerging technologies
  • Research contributions discussing implementation success and failure stories
Minitrack Co-Chairs:

Johnny Chan (Primary Contact)
University of Auckland

Gabrielle Peko
University of Auckland

David Sundaram
University of Auckland

Ghazwan Hassna
Hawaiʻi Pacific University

This minitrack welcomes papers addressing issues that arise in designing, building and evaluating cellular and wireless networks, and on the applications of cellular and wireless networks in solving real-world challenges and securely connecting users and devices. These networks include cellular networks (6G, 5G, 4G, 3G, and 2G), Wi-Fi networks, sensor networks, ad-hoc networks, Bluetooth, optical networks, and more. The breadth of this minitrack includes technical, operational, social, environmental and other issues.

Technical issues are found in the protocol stack from the application layer to the physical layer, including security, efficiency, scalability, the design and motivation of new systems, novel applications, better use of existing technology, energy efficiency of communications, regulatory issues, and in general, issues that are of concern when designing or building cellular and wireless networks. Since relevant operational, social, environmental, and economic considerations are frequently essential to the success of the technology, appropriate non-technical papers are also welcome in this minitrack.

The following is a partial list of research topics of interest for this minitrack:

  • 6G, 5G, 4G, 3G, and 2G cellular networks
  • Wi-Fi networks
  • Ad hoc wireless networks including mesh networks
  • Wireless communication for the Internet of Things (IoT) and Vehicle Ad-hoc Networks (VANETs)
  • Wireless optical or underwater networks
  • Wireless communication for home automation
  • Security in wireless networks
  • Protocols and algorithms for physical, MAC, routing, transport, and application layers, including cross-layer techniques
  • Theoretical issues of interest in the design or implementation of wireless networks, including communications, scalability, coordination, access control, and other advances in the field, such as millimeter wave technologies
  • Novel applications of wireless networks
  • Social and environmental benefits and consequences of the use of wireless networks and new applications.

In general, this minitrack is expansive in welcoming submissions in any area related to cellular and wireless networks. Prospective authors are encouraged to contact the minitrack chairs if they seek more detailed guidance.

Minitrack Co-Chairs:

Edoardo Biagioni (Primary Contact)
University of Hawaii at Manoa

John McEachen
Naval Postgraduate School

Murali Tummala
Naval Postgraduate School

Cognitive Cloud is an enhanced Cloud-Fog-Edge system that is capable of sensing its environment, learning from it, and opportunistically and dynamically adapting its computational loads to the user intents. Here, the core enabling technologies, for management of resources, services and data are AI/ML techniques, which infuse the cognitive aspects into the continuum.

This minitrack intends to solicit papers that discuss the theoretical and practical aspects of Cognitive Cloud Ecosystems from a system perspective. Topics of interest include:

  • Architectures for the Cognitive Cloud for systems that are user-aware, self-aware and (semi-)autonomous; address the need for real-time capable solutions; and solve performance challenges, such as data streaming and filtering near/at the edge, overcoming latency and network constraints
  • Service mesh networking that controls service-to-service communication across cognitive cloud ecosystem
  • Orchestration of (sub)systems in the Cognitive Cloud, possibly subdivided into orchestration of resources, services, and data (including zero-touch approaches)
  • Use of distributed AI across the Cognitive Cloud to make it intelligent
  • Interconnection of Cognitive Clouds with Data Spaces
  • Security, privacy and trust (likely, by design) in a multi-tenet de-centralized systems, possibly with no single point of governance
  • Interoperability across the Cognitive Cloud Ecosystems to cope with the increased complexity of vast numbers of heterogeneous devices, while supporting demands for data sharing combined with the demand for protection of privacy
  • Role of intelligent devices, drawing from applicable results in micro/nano/bio technologies, including resource-aware hardware/software concepts, low power processor platforms integrating computing, networking, storage and acceleration elements, new communication schemes and topologies that range from the cloud continuum towards mesh, and securing computing and communication at device level with constrained resources
  • Tactile/contextual Internet of Things based on human-centric sensing/actuating, augmented/virtual reality and new service capabilities such as integration with parallel and opportunistic computing capabilities, neuromorphic and contextual computing.
  • Energy aware systems from the perspective of systems integration for efficient deployment of services and use of resources in the Cognitive Cloud
  • Strategies for the deployment of hyper-distributed applications in the Cognitive Cloud from services description to dynamic reorganization of the deployment
  • Extreme data processing applications and Frugal AI in the cognitive cloud
  • Applications of Cognitive Cloud including implementation of ecosystems, pilots, lessons learned, barriers, etc.
Minitrack Co-Chairs:

Marcin Paprzycki (Primary Contact)
Polish Academy of Sciences

Maria Ganzha
Polish Academy of Sciences

Harilaos Koumaras
Institute of Informatics and Telecommunications

Carlos Palau
Univeristat Politècnica de València

This minitrack embraces deep dives from the numerous disciplines that are required for a rigorous, effective, and innovative approach to developing, operating, and securing cyber systems across Information Technology (IT) and Operational Technology (OT) spaces. Addressing challenges presented by an evolving threat landscape and the increasing connected-ness of critical infrastructure systems requires a coordinated multi-disciplinary approach. These disciplines include math, computer science, electrical engineering, data and information science, systems engineering, human factors, and more.

We seek high-quality research papers that apply the principles of scientific inquiry or engineering in any of these areas. Special consideration for acceptance will be given to those papers that apply the scientific method to a current cyber challenge, demonstrate rigorous experimental design, thorough analysis of results, detailed visualization of results where appropriate, and concrete conclusions and recommendations for future work. We especially encourage papers that emphasize cross-disciplinary approaches to cyber challenges.

A partial list of topics of interest, especially with alignment to critical infrastructure, includes:

  • In-progress results in cutting-edge, high-risk, high-reward cyber research
  • Cross-disciplinary approaches to cyber security or trust
  • Cryptography, cryptanalysis, privacy, and security
  • Machine learning, artificial and augmented intelligence applied to cyber systems
  • Network anomaly detection and novel approaches to securing OT
  • Modeling and simulation of cyber systems, security, trust, and risk
  • Human-machine interaction and optimization
  • Securing the cloud
  • Cyber system adaptation, organization, optimization, and resilience
  • Intrusion detection systems and AI-augmented IDS
Minitrack Co-Chairs:

Chad Bollmann (Primary Contact)
Naval Postgraduate School

Britta Hale
Naval Postgraduate School

James Scrofani 
Naval Postgraduate School

Nick Tsamis
MITRE Corporation

As technology is incorporated into more aspects of daily life, cyber operations, defenses, and digital forensics solutions continue to evolve and diversify. This encourages the development of innovative managerial, technological, and strategic solutions. Hence, a variety of responses are needed to address the resulting concerns. There is a need to research a) technology investigations, b) technical integration and solution impact, c) the abuse of technology through attacks, along with d) the effective analysis and evaluation of proposed solutions. Identifying and validating technical solutions to secure data from new and emerging technologies, investigating these solutions’ impact on the industry, and understanding how technologies can be abused are crucial to the viability of commercial, government, and legal communities.

We welcome new, original ideas from academia, industry, government, and law enforcement participants interested in sharing their results, knowledge, and experience. Topics of interest include but are not limited to:

  • Detection and analysis of advanced threat tactics, techniques, and procedures
  • Applying machines learning tools and techniques in terms of cyber operations, defenses, and forensics
  • Case studies surrounding the application of policy in terms of cyber operations, defenses, and forensics
  • Approaches related to threat detection and Advanced Persistent Threats (APTs)
  • Solutions that secure different types of data stored in different layers of computer networks
  • “Big Data” solutions and investigations – collection, analysis, and visualization of “Big Data” related to cyber operations, defenses, and forensics
  • Malware analysis and the investigation of targeted attacks
  • Device investigations that assist with the recovery and reconstruction of digital artifacts
  • Digital evidence recovery, storage, preservation, and memory analysis
  • Event reconstruction approaches and techniques
  • Anti-forensics techniques and solutions
  • Investigations related to mobile devices, embedded systems, or Internet of Things (IoT) devices
  • Forensic investigations within emerging domains such as transportation systems, industrial control systems, and SCADA
  • Network investigations – collection, analysis, and visualization of network forensic data
  • Privacy implications related to security incident response and digital forensic investigations
  • Research in security incident management
  • Situational awareness related to security incident response
  • The impact of digital evidence on the legal system

The above list is suggestive, and authors are encouraged to contact the minitrack chairs to discuss related topics and their suitability for submission to this minitrack.

Accepted papers will be offered the opportunity to extend their submission by 50% and submit to a special issue of the Association for Computing Machinery (ACM) Digital Threats: Research and Practice (DTRAP) Journal.

Minitrack Co-Chairs:

William Bradley Glisson (Primary Contact)
Louisiana Tech University

Todd McDonald
University of South Alabama

Philip Menard
University of Texas at San Antonio

Modern society is irreversibly dependent on software systems of astonishing scope and complexity. Yet despite best efforts, errors, vulnerabilities, failures, and compromises continue to persist. Networked systems with complex hardware and software components embody many pathways that adversaries can exploit. Experience shows that contemporary cybersecurity and software assurance methods are insufficient to meet this challenge.

This minitrack focuses on how to enable development and application of these foundations. We ask: How should research and development move us toward a solid basis in understanding and principle? The goal is to develop science foundations, technologies, and practices that can improve the security and dependability of complex systems. This minitrack will bring together researchers in cybersecurity and software assurance in a multidisciplinary approach to these problems.

Our minitrack invites work embracing multiple perspectives, levels of abstraction, and evaluation of best practices and policies that help us to understand and assure the security of complex systems. We welcome papers about tools and techniques in that apply scientific and rigorous approaches or reveal underlying commonalities and constructs. The following topics will be included in the minitrack:

  • Security ecosystem
  • Designed-in security
  • Tailored trustworthy spaces
  • Moving target
  • Cyber economics
  • Science of security
  • Multivariate detection and response
  • Co-evolution of defense and offense
  • Biologically-inspired security models
  • Holistic risk analysis
  • Hardware-enabled trust
  • Layered adaptable defense
  • Real-time coordinated response
  • Automated system interoperability
  • Authentication in ecosystem
  • Practical use of continuous monitoring
  • Confidence in activity prediction
  • Security visualization and prediction
  • Theories of vulnerability classification and control
  • Security measurement
  • Advances in information assurance theory and practice
  • Advances in specification, design, and implementation of assured systems
  • Advances in verification, testing, and certification of assured systems
  • Advances in software security analysis
  • Assurance for embedded systems and hardware components
  • Assurance for large-scale infrastructure systems
  • Information and software assurance in cloud computing environments
  • Assurance in system maintenance and evolution
  • Automated methods for information and software assurance
  • Assurance through computation of software behavior
  • Management of assurance operations
  • Processes and metrics for information and software assurance
  • Business case and ROI development for information and software assurance
  • Supply chain and standards issues in information and software assurance
  • Case studies of system assurance successes
  • Software testing
Minitrack Co-Chairs:

Luanne Burns Chamberlain (Primary Contact)
Johns Hopkins University Applied Physics Lab

Thomas Llanso
John Hopkins University Applied Physics Lab

Richard George
Johns Hopkins University Applied Physics Lab

The world is witnessing an unprecedented digital trust crisis impacting many aspects of our daily lives. While regulatory frameworks such as GDPR are now in place and have a global impact, public policies and global digital governance frameworks are only emerging. Corporate Digital Responsibility in this context has now emerged as an important issue in the broader context of environment, social, and governance issues (ESG). The industry is too often left in the driving seat, proposing its systems and services with limited concern for digital responsibility issues such as data protection, privacy, algorithmic bias and transparency, ethical and social issues, etc. Often, this leads to self-regulation approaches that have proven not to work. Worse, sometimes industry initiatives try to impose norms that serve their sole interests. Society is consequently left in a position of digital vulnerability. Industries and nation states have a responsibility in this context of digital transition towards designing a sustainable and responsible digital society.

Digital Responsibility can be decomposed in three dimensions. First, Corporate Digital Responsibility (CDR) focusing on organizations and how they engage along several key digital issues they should be held responsible if not accountable with respect to their business. Several frameworks and approaches now exist in that space covering mostly the same issues. Unfortunately, they remain in the form of pledges or manifestos with limited impact. Second, Public Digital Responsibility covers all the policy and regulatory frameworks governing the development of our digital transition to ensure the ecosystem is developing according to sustainable and responsible guidelines. Finally, Personal Digital Responsibility ensuring people evolve in the digital realm with a minimal level of understanding and hygiene.

In this context, focusing on the industry and on the technical mechanisms such as design patterns, models, algorithms, frameworks, etc. supporting digitally responsible software engineering and system design, will represent a true contribution towards a more sustainable digital society.

Here AI deserves special attention. The spread of AI generated disinformation in the form of chatbots, deepfake videos, audios, and voice, etcetera, pose a serious global problem. The AI researchers and practitioners must create the techniques to detect disinformation when it exists and taken as a group embody the Personal Digital Responsibility, the ethics to only create AI that benefits humanity.

To this end, we invite contributions from researchers and practitioners in academia and industry on novel approaches supporting more responsible and sustainable system and service design and engineering. A general list of topics of submissions include, but are not limited to:

  • Design patterns supporting responsible and accountable software engineering covering the key challenges of digital responsibility
  • Technical approaches, algorithms or frameworks providing support in identifying, reporting, supporting, and implementing digital responsibility
  • Methodological support for system and service design
  • Design of digital technology addressing Digital Responsibility challenges at large
  • Theoretical and/or empirical studies on digital responsibility issues in system and service design
  • The algorithms necessary to detect and eliminate the ever growing list of AI malware that is becoming a new millennium plague
Minitrack Co-Chairs:

William Yeager (Primary Contact)
Stanford University Knowledge Systems Lab, Retired

Jean-Henry Morin
University of Geneva

The idea of creating digital twins as connected virtual counterparts for physical objects becomes increasingly popular and may soon be a key factor in enterprise success across the industries. Nowadays, the concept is already applied in various application fields such as engineering, manufacturing, e-commerce, social media platforms or health care. As a consequence, digital twins often already directly or indirectly affect the work and daily life of humans. Therefore, it is also important to consider the effects of digital twins from the perspective of other fields like psychology, sociology, or organizational management.

This minitrack intends to gather an overview of the current state in creating and managing digital twins as well as the application of digital twins. Based on this, future perspectives and directions for applications and research shall be explored. The following topics are of particular interest:

  • Methods for creating, analyzing, simulating, storing and querying digital twins
  • Platforms for digital twins
  • (Federated) models of digital twins
  • Digital Twin applications (e.g., smart cities, smart buildings, Car-as-a-Service (CaaS), enterprises, future of work, …)
  • Impact of digital twin applications on industries, work, humans, and society
Minitrack Co-Chairs:

Tim Schattkowsky (Primary Contact)
University of Applied Sciences Hamm-Lippstadt

Gregor Engels
University of Paderborn

Achim Rettberg
Carl-von-Ossietzky Universität Oldenburg

Generative AI is a type of artificial intelligence (AI) that generates new and original content based on patterns learned from existing data. This can include a range of media such as images, videos, audio, and text. Conversational AI focuses on enabling natural language interactions between humans and AI systems. It uses NLP (Natural Language Processing) and machine learning algorithms to understand and respond to human language input.

The growth of generative and conversational AI and the development of large language models like ChatGPT has created new possibilities for applications in various fields, including information systems research and education. This minitrack aims to explore the use of Generative and Conversational AI models in information systems, including natural language processing, recommendation systems, and personalization. The minitrack will be of particular interest to researchers and practitioners from the fields of information systems and Generative/Conversational AI, as well as those teaching information systems courses interested in incorporating Generative/Conversational AI into the information systems curriculum.

  • Applications of Generative AI in Information Systems, such as data generation, image generation, text generation, video generation, and simulation
  • Applications of Conversational AI in Information Systems, such as chatbots, virtual assistants, and voice assistants, among others
  • Best practices for integrating Generative and Conversational AI into the Information Systems curriculum
  • Developing educational materials and resources for teaching Generative and Conversational AI
  • Preparing students for careers in Generative AI and Information Systems
  • Integrating Generative AI with other AI technologies for improved results
  • Real-world applications of Generative AI in Information Systems research and education.
  • Generative AI for personalized recommendations and decision-making
  • Generative AI for improving the efficiency of Information Systems
  • Generative AI for enhancing user experience in Information Systems
  • Generative AI in various domains, such as healthcare, finance, and entertainment
  • Use of ChatGPT in natural language processing and information retrieval
  • Ethics, privacy, and regulatory considerations for the use of Generative AI in information systems
  • Methodologies and frameworks for evaluating the effectiveness and impact of Generative and Conversational AI in Information Systems
  • The impact of Generative and Conversational AI on society and the economy
  • The effects of generative AI, such as ChatGPT, as an enabler versus a disabler
  • Challenges and limitations in the implementation of Generative and Conversational AI in Information Systems
  • Detection of AI generated content
  • The platform governance relating to Generative and Conversational AI
  • Dark side of Generative and Conversational AI, such as misinformation and fake news on social media
  • Augmented Intelligence: Using Generative AI to enhance human capabilities
Minitrack Co-Chairs:

Nargess Tahmasbi (Primary Contact)
Pennsylvania State University

Elham Rastegari
Creighton University

Guohou (Jack) Shan
Temple University

Aaron M. French
Kennesaw State University

We would like to invite papers which focus on developing paradigms for addressing the synergy of software, devices, moveable objects, networks, and people, in one constantly changeable world of pervasive computing, where computational and human intelligence are spreading everywhere. We would like to debate challenges of empowering edges of computer networks and misconceptions on their capabilities of carrying out modern computations. We would like to analyse examples of dynamic edge computing and look at the future of the IoT and internet of everything in particular. Their speedy transformation is fascinating. They are constantly changeable sets of new types of connected devices, typical of pervasive computing, which might be wearables, belong to soft robotics, represent computational materials, and enable human machine augmentations and teaming. In all these examples, edge computing plays a very important role, and it is no surprise that we have started thinking how to equip the edges of computer networks with intelligence.

There are many challenges in promoting edge computing. Most of them stumble upon the potentially uncertain future of replacing powerful cloud computing with new paradigms in which intelligent edge computing may take centre stage. Therefore, it is worthwhile exploring what exactly edge computing brings to the world of pervasive computing and how we could make this new computing paradigm become “intelligent edge”. Topics of interests include:

  • Towards Edge Computing Paradigm
    • Assessing computational edge technology landscape and its potentials for creating new computational paradigms for the edge
    • Analysis of computability within edge technology landscape: the power of data and scalability of computations
    • Promoting localised computing, with Edge/Cloudlets/Fog in mind: examples and implementations
    • Evaluating computational models for Cloud computing and their suitability in pervasive environments in general
    • Pushing data and computational intelligence, with analytic platforms, away from centralised Clouds and the role of serverless computing
    • The current role of Edge/Fog computing in IoT applications and implementations: reconsidering the role of Clouds
    • Examples of computing paradigms suitable for localised computing and its extensions towards Fog/Cloudlets/Clouds
  • Creating Intelligent Edge Computing
    • Roadmap for AI at the computational edge, challenges, and opportunities
    • Feasibility of creating intelligence “at the edge” which could share computations and data between Clouds, Fog and Cloudlets
    • Using learning and predictive technologies by defining models for machine learning algorithms and types of AI solutions which could create intelligent computational edge and address its constraints
    • Software architectures for supporting intelligence at the computational edge for the purpose of creating solutions in automation, manufacturing, businesses, medicine, healthcare delivery, education, and governance
    • Potential convergence of humans,” things” and AI in creating edge intelligence
    • Possibilities of defining a computational continuum by utilizing the space between localized computing and clouds and thus enhance the intelligence at the edge
  • Edge Computing for/in Human Augmentation and Teaming, Internet of Materials and Human centered Cognitive IoT
    • The role of edge computing in creating augmentation and teaming of human with machines and vice versa
    • Promoting intelligent engineering for designing cognitive devices ready to accommodate edge intelligence
    • Investigating computational materials, shape changing computing particles and internet of materials as potential factors for empowering intelligent edge and enhancing pervasive computing
    • Pushing forward the fusion of computational materials with hardware/software synergy typical of binary/ternary/quantum computers
    • Cognitive enabled edge computing and a journey towards human centered cognitive IoT, and pervasive environments in general
Minitrack Co-Chairs:

Radmila Juric (Primary Contact)
ALMAIS Consultancy

Elisabetta Ronchieri
IFNF/CNAF Bologna and University of Bologna

Filippo Sanfilippo
University of Agder

The objective of this minitrack is to increase the visibility of current research and emergent trends in Cyber-Assurance theory, application, embedded ‘zero trust’ security, artificial intelligence, and machine- learning for the Internet of Things (IoT), 5G, zero trust, and Edge computing architectures based on theoretical aspects and studies of practical applications.

Cyber-assurance is the justified confidence that networked systems are adequately secure to meet operational needs, even in the presence of attacks, failures, accidents, and unexpected events. Cyber-assurance means that 5G IoT systems, smart internet connected devices (ICD) and networks provide the opportunity of automatically securing themselves against cyber-attacks. The difference is that the concept of cyber-assurance must provide embedded zero trust principles (i.e., complete mediation) in ICD devices and virtual networks that can continue to operate correctly even when subjected to an attack.

IoT devices rely heavily on 5G, and Edge computing systems and networks should be able to resist the various security cyber-attacks such as hacking of networks, devices, theft of information, disruption, etc. and be able to continue performing under severe environmental conditions. Through embedded processors and machine learning algorithms over the transmitted information, the miscoding and leaking of information during transmission channels must monitor any loss, miscoding and leaking of data. Timely adjustments of information with falling quality and automatic switching to the best routing IoT systems by making uses of multi-directional routing is also warranted. Cyber-assurance will need to provide the principles and technologies to unify these systems to deliver the end-state goal of secure IoT systems for greatly enhanced interoperability, scalability, performance, and agility.

This minitrack will focus on the architecture and security needs of these environments, highlighting key issues and identifying the associated security implications so that the general participates can readily grasp the core ideas in this area of research. Recommended topics for this track include, but are not limited to, the following:

  • Edge computing with 5G
  • Zero trust architectures and designs for 5G, IoT, and Edge computing systems
  • Implementing 5G security mechanisms in pervasive/ubiquitous computing
  • Secure Cloud and Fog layer interaction through Edge networks.
  • Implementing privacy and security in 5G, IoT, and Edge devices and users.
  • Experimental results on spectrum effectiveness in End-to-End 5G systems.
  • Machine-to-Machine communication in 5G architectures and services.
  • 5G joint management and orchestration of IoT networking with cloud technologies.
  • Research on automated 5G networks intrusion detection tools and techniques.
  • A.I./Machine learning algorithms used to spawn and control intelligent agents for information assurance for 5G, IoT, and Edge computing devices and networks.
  • Security mechanisms for 5G , IoT, and Edge Networks.
  • Quantum algorithms and architectures for 5G, IoT, and Edge computing.

There will be opportunities for researchers to fast track a journal publication in a Special Issue with the International Journal of Internet of Things and Cyber-Assurance (IJITCA). Dr. Brooks is the Editor-in-Chief of the IJITCA.

Minitrack Co-Chairs:

Tyson Brooks (Primary Contact)
Department of Defense and Syracuse University

Shiu-Kai Chin
Syracuse University

Recent advances in computing technologies together with rapid growth of high-speed telecommunication networks have led to significant interest in research in machine learning (ML) algorithms and artificial intelligence (AI) applications in the domain of cybersecurity. Cyber threat hunting, which entails searching through SIEM (Security Information and Event Management System) logs to locate potentially malicious activities that evade existing security solutions, is one area that can benefit from ML and AI.

Academic research as well as organizational approach to cybersecurity has been predominantly either preventive or reactive. Recently, there is a significant interest in designing cybersecurity systems adopting hybrid approaches that leverage strengths of both these approaches by accumulating knowledge, increasing signature intelligence, and reducing false positive rates.

Cyber threat hunting is a proactive approach that is critical for many organizations and helps identify potentially malicious activities at the outset and stop them in their tracks. However, searching and identifying cybersecurity threats or potential malicious activities is a time-consuming and labor-intensive process. Use of ML and AI can be helpful but challenging due to the amount of data that need to be analyzed with little to no prior knowledge of the emerging nature of threats or malicious activities.

This minitrack aims to provide insights into proactive approaches to cybersecurity by developing a theoretical understanding of technical as well as behavioral issues related to and insights from practical approaches used in industry without excluding any methodological approaches. We welcome conceptual, theoretical, empirical, experimental, methodological, and practice-based papers that enrich our understanding of current uses of ML and AI in cybersecurity in general, and in proactive approaches to cybersecurity. Topics of interest include, but are not limited to:


  • Ethical Hacking
  • Machine Learning Applications in Cybersecurity
  • Automated Intelligence
  • Zero Trust Approaches
  • Proactive Versus Reactive in Endpoint Monitoring
  • Indicators of Compromise
  • Insider Threats and Cybersecurity Policies
  • Proactive System Behavior Analysis
  • Threat Hunting
  • Modeling Threats
  • Proactive Approaches to Ransomware
  • Game Theoretic Modeling of Cyberattacks
  • Cyber Attack Mitigation
  • Cyber Threat Intelligence
  • Digital Forensics
  • Privacy Preserving Artificial Intelligence
  • Explainable Artificial Intelligence in Cybersecurity
  • Evaluation metrics relevant to Cybersecurity
  • Adoption and Use of Artificial Intelligence Tools in Cybersecurity
  • Resilient Cybersecurity Systems

Selected papers will be provided with a fast track opportunity to the a Special Issue of the Information Systems Frontiers journal published by Springer.

Minitrack Co-Chairs:

Varol Kayhan (Primary Contact)
University of South Florida

Shivendu Shivendu
University of South Florida

Manish Agarwal
University of South Florida

David Zeng
Dakota State University

Quantum computing is a rapidly evolving field with a wide range of applications in diverse industries. Hybrid quantum computing combines classical and quantum computing to solve complex problems, quantum machine learning is a subset of machine learning that leverages quantum computing for optimization, and quantum embeddings use quantum algorithms to process high dimensional data. Quantum computers have the potential to revolutionize finance, industry, production, drug research, and even consumer-facing services. With quantum computing’s ability to handle massive amounts of data and perform complex calculations at incredible speeds, it holds great promise for addressing some of the world’s most pressing problems in these areas. The minitrack invites researchers from computer science, natural sciences, engineering, and economics fields to submit their research on applications of quantum computing.

Topics of interest include, but are not limited to:

  • Quantum simulation and modeling
  • Quantum optimization and control
  • Quantum artificial intelligence and deep learning
  • Quantum image processing
  • Quantum chemistry and materials science
  • Quantum finance and economics
  • Quantum computational biology and drug discovery.
  • Quantum sensing and metrology
  • Quantum chemistry and materials science
  • Quantum game theory and decision making
  • Quantum cryptography
Minitrack Co-Chairs:

Wolfgang Maass (Primary Contact)
German Research Center for Artificial Intelligence (DFKI)

Frank Wilhelm-Mauch
Peter Grünberg Institut (PGI), Forschungszentrum Jülich GmbH

Frederick Struckmeier

Information security and privacy are a non-negotiable factor in the design and operation of information systems. Especially users – the so-called human factor – are a pivotal role in information security and user-privacy concepts. Often, their knowledge about security aspects and ways of user-manipulation tactics are the last line of defense against cyber-attacks. However, they are also the primary target of attackers and need to be sensitized about security-compliant behavior.

In addition to the traditional forms of user-computer-interactions in the form of mouse- keyboard-input-devices, new ways of system-interactions, e.g., physiological data from fitness-trackers, eye-tracking devices or even pupillary responses indicating cognitive-load- levels, are increasingly feasible as everyday HCI-components. With the interest in data privacy increasing, are users aware how valuable those personal input data is and how do they value data privacy measures?

Therefore, we have identified two main aspects relevant to researchers within the domain of Software Technology: 1) how to securely deal with input data (also focusing on privacy aspects) 2) how this data can be utilized to increase secure behavior or to raise awareness among users (help the users to make better security-related decisions)

In this minitrack, we seek papers that explore concepts, prototypes, and evaluations of how users interact with information systems and what implications these interactions have for information security and privacy. Further, we welcome new and innovative ways of human- computer-interaction and security-related concepts currently examined in the field. Topics of interest include but are not limited to:

  • Security related devices
  • Physiological sensors
  • Human-Computer-Interaction
  • (Conversational) Artificial intelligence
  • Blockchain applications
  • Sensor analysis
  • Data visualization
  • Biometrics authentication
  • Security and privacy awareness
  • Accessibility
  • Usable security design
  • Privacy and security by design
  • Privacy and smart contracts
  • User valuation of privacy
  • Validation of user data
Minitrack Co-Chairs:

Nicholas Müller (Primary Contact)
Technical University of Applied Sciences Würzburg-Schweinfurt

Kristin Weber
Technical University of Applied Sciences Würzburg-Schweinfurt

Paul Rosenthal
University of Rostock

Self-adaptation is often seen as an emergent property in self-organizing systems, where adaptation mechanisms are not explicitly defined. Adaptivity is the capacity to learn and adapt to its environments, especially considering various uncertainties. Various research streams address relevant aspects of adaptation, such as Self-adaptive and Self-organizing Systems, Pervasive/Ubiquitous Computing, Autonomic Computing, Multi-agent Systems, Cyber-Physical Systems, Organic Computing, Self- aware Computing Systems, Autonomous Systems, etc.

A special emphasis of this minitrack is the inclusion of principles for engineering (self-)adaptive systems and applications in constantly changing environments to facilitate reducing (re-)configuration, troubleshooting, or maintenance during the operating phase.

This includes papers from the academic and practitioner community that propose technical solutions in various domains, e.g., smart-manufacturing, -logistics, -mobility, communication or –waste management. Furthermore, we are looking for conceptual contributions dealing with novel aspects of systems engineering and applications. We want to determine how these domains can profit from self- adaptive systems and applications.

Among others, challenges of self-adaptive systems include dealing with uncertainties/model drift, communication overhead, adaptation decision criteria, degree of decentralization, and human-in-the- loop interaction. This minitrack searches for papers on approaches to tackle these and other challenges. We anticipate submissions not limited to but in the scope of the following topics:

  • Engineering aspects concerning:
    • Machine Learning for Self-Adaptive Systems and Applications
    • Self-configuring, -healing, -protecting and -optimizing Systems
    • Decentralization, coordination and collaboration of autonomous agents
    • Autonomic and Self-* system properties: robustness, resilience, efficient resource management, stability, emergent behavior, computational and self-awareness
    • Autonomic and Self-* systems engineering: reusable mechanisms and algorithms, design patterns, architectures, operating systems and middlewares, runtime models
  • Applications in various fields, among others:
    • Internet of Things, Edge Computing, and Cyber-Physical Systems
    • Industry 4.0, Cyber-Physical Production Systems, and Automation and Autonomization in (Intra-) Logistics
    • Smart environments: -grids, -cities, -homes, and -manufacturing
    • Robotics and Automation, smart mobility, and intelligent transportation systems
    • Cloud (including serverless), fog/edge computing, High Performance Computing (HPC), quantum computing and data centers
    • Smart data models (harmonization of data for portability for different applications)
    • Intelligent network communication
  • Cross disciplinary research: approaches that draw inspiration from complex systems, artificial intelligence, physics, chemistry, psychology, sociology, biology, and ethology
Minitrack Co-Chairs:
Peter Detzner (Primary Contact)
Fraunhofer Institute for Material Flow and Logistics (IML)

Sören Kerner
Fraunhofer Institute for Material Flow and Logistics (IML)

Christian Krupitzer
University of Hohenheim

Christian Becker
University of Stuttgart

Building Smart (City) Applications has been a research domain for several years now. However, what we are interested in, in the context of this mini-track, is how the concepts and ideas of Smart Applications are realized, especially from a software engineering point of view. Thus, we are looking for manuscripts on planned, currently developed, and/or already finished Smart Application, IoT and Industry 4.0 projects, feasibility studies of all domains, and evaluations of such applications in the real world. We want to see which architectures, frameworks, platforms and infrastructures seem to be particularly successful.

The user community for smart applications is characterized by different stakeholders: from researchers to policy makers to the general public. Thus, it is crucial to achieve easy-to-use end-to-end solutions, which support the diverse roles in these communities and their different interests and knowledge about smart applications. Such concepts are considered in so-called science gateways (also known as virtual laboratories or virtual research environments), which by definition serve communities with end-to-end solutions tailored specifically to their needs.

Software challenges for smart applications are broad, not only in regard to the breadth of the community, but also to the breadth of topics. A specific focus of this mini-track is on how to deal with the details of scenarios like handling huge data sets, high usage loads, complex event processing on real-time data streams, sharing data between applications, security and so forth. We also look for disruptive technologies, which influence Smart (City) Applications development, e.g. 5G networks, Spatial Computing, Blockchain, Conversational Computing and Autonomous Vehicles. Consequently, we anticipate submissions not limited to but in the scope of the following topics:

  • System Architectures for Smart Application and Industry 4.0
  • Best practices and Key Success Factors in Smart Application Development
  • Platforms for Smart Applications
  • Software for edge2cloud Continuum
  • Smart Applications for Smarter Government
  • Data-hubs and their roles in Smart Application Developments
  • Easy-to-use end-to-end solutions for Smart Applications
  • Web services for Smart Applications
  • Real-time data analytics and machine learning in practice
  • Successful Smart Application Project Management
  • Infrastructures for Smart Applications
  • Securing Smart Applications and Sensor Networks
  • Promoting Smart Applications
  • Digital Twin technology for Smart Cities
Minitrack Chair:

Sandra Gesing (Primary Contact)
University of Illinois Discovery Partners Institute

Peter Salhofer

Cezary Mazurek
Poznan Supercomputing and Networking Center

Charlie Catlett
Argonne National Laboratory and the University of Chicago

The focus on software usability, long-lasting and reproducible software is a timely one that spans various domains of science and significant investment of research funding both in the US, Europe, U.K, and elsewhere. Software has become a major driver for research with over 90% of researchers answering surveys that they use software for their research and over 65% expressing that they even could not do their research without software. The computational landscape has evolved from system-centered design focusing on training users to user-centered design delivering solutions that are intuitive and/or self explanatory. The prominence of research software creates challenges in the following areas – usability and ease of use, sustainability, and reproducibility. Thus, the concept of long-lasting, easy to use software accelerating science is a major concern for researchers. Additionally, researchers would like to be able to re-use software technologies to be able to analyze further data with established and verified methods, which is part of reproducibility approaches.

The three concepts usability, sustainability and reproducibility are interconnected with each other and cover a wide range of application areas. They affect all layers of the software process – from enabling reproducing experiments via an easy user interface to using containerization for application portability. Such concepts are also relevant in the building of Science Gateways (also known as virtual laboratories or virtual research environments), which by definition serve communities with end-to-end solutions tailored specifically to their needs. The mini-track will focus on the broad spectrum of submissions that deal with complex scenarios such as containerization, strategies for long-lasting software, usability and user interface issues, handling data curation and provenance and more.

The minitrack will focus on the broad spectrum of submissions that deal with complex scenarios such as containerization, strategies for long-lasting software, usability and user interface issues, handling data curation and provenance and more. Consequently, we anticipate submissions not limited to but in the scope of the following topics:

  • Web-based solutions (web sites, science gateways, virtual labs, etc.)
  • Application Programming Interfaces (APIs)
  • Computational and Data-Intensive Workflows
  • Novel approaches in containerization
  • Sustainability practices in software development
  • System architectures for testing and continuous integration
  • Emerging best practices in Machine Learning software
  • Best practices and Key Success Factors for usability, sustainability and reproducibility
  • Community building practices
Minitrack Co-Chairs:

Maytal Dahan (Primary Contact)
The University of Texas at Austin

Joe Stubbs
The University of Texas at Austin

Sandra Gesing
University of Illinois Discovery Partners Institute

The development of software has provided ample opportunities for research, provides ample opportunities, and likely will provide ample opportunities. Not long ago, the proliferation of mobile computing opened up a new stream of research, then the same happened with the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS). Possibly, fog, edge, and dew computing and the convergence of technologies will continue this trend. All of these topics seemingly provide completely new endeavors; with a closer look, however, they can draw from what is already known – both regarding typical problems and regarding solutions.

Experiences and methods from classical software development can only be utilized to some degree when solving challenges that arise from new application, changing environment, and demanding domains. Development is complicated by the often faced need to develop for a multitude of platforms. With the emergence of multi-platform and multi-device, the new golden standard are applications not only across software ecosystems, but across hardware platforms such as laptop, mobile, tablets, embedded devices, sensors, and wearables. Therefore, new threads of research are needed to tackle these issues and to pave the way for improved software development, better business producibility and improved user experience (UX).

Further, there are novel developments in machine learning and analysis, and the emergence of multi-faceted aspects of artificial intelligence (AI), ranging from algorithms to ethical AI, secure AI and sustainable AI perspectives. This creates new opportunities for groundbreaking research through distributed machine learning, federated learning, edge analytics and computational collaboration between several heterogenous systems and device forms.

This minitrack invites researchers from software engineering, human-computer interaction, information systems, computer science, electrical engineering, and any other discipline that contributes to how software is designed, implemented, tested, and deployed. It is devoted to the technological background while keeping an eye on business value, user experience, and domain-specific issues. Contributions may take a socio-technical view or report on technological progress. We are particularly interested in applied software technology but also welcome theoretical work. Topics of interests include, but not limited to:

  • Case studies of development
  • Development methods, software architectures, and specification techniques
  • Economic and social impact, behavioral aspects
  • Software engineering education
  • User interface (UI) design and user experience (UX) research
  • Hybrid and cross-platform development
  • Web technology
  • Security, safety, and privacy
  • Energy-efficient computing
  • Machine learning on device
  • The convergence between mobile devices, IoT, and CPS
  • Fog, edge, and dew computing and their computational applications
Minitrack Co-Chairs:

Tim A. Majchrzak (Primary Contact)
University of Agder

Tor-Morten Grønli
Kristiania University College

Hermann Kaindl
TU Wien

Call for Papers

With the advancement of AI technology, AI algorithms start to match human performance for certain tasks (e.g. ChatGPT) and discover loopholes in systems that were not previously found. AI in general and ML methods specifically are increasingly used with scientific data and applied with great promise to solve a great variety of problems. These can be as diverse as control of operations at facilities and computing and data centers, for sifting through the millions of combinations that can produce viable candidates for experiments, showing potential for autonomous experiments and experimental design.

However, researchers need to understand how Artificial Intelligence (AI) and Machine Learning (ML) results are obtained in order to gain new insights and to establish confidence in the validity of these results. Otherwise, the promises of AI/ML will not be realized if scientists cannot trust the results, understand how they were obtained, gain transparency into what datasets, models and model parameters have been used or what features of the data lead to these results. Like any good scientific results, also AI/ML pipelines should be reproducible to the most possible extent.

With the increased use of AI comes an increase in the inherent complexity of the models. Deep Learning (DL) models with millions of nodes and degrees of freedom operating with large data volumes obscure their inner workings to human understanding. Unlike traditional ML algorithms such as rule-based decision trees or linear-regression models where the decision boundary is clear, the DL models consist of very large amounts of learned parameters. Therefore, interpreting a learned model is difficult.

The incredible growth in scale of AI training models, the use of heterogeneous architectures, and the development of generative adversarial models, complexity and the need for transparency are compounded by the need to avoid bias in predictions. Numerous examples of bias have been discovered in image recognition, classification, and text generation. Thus, formal explanations of how models achieve results, explicit representations of data, comprehensiveness and diversity of datasets used for training are crucial to foster trust in AI. Additionally, as AI models are inherently stochastic, experiments show that results obtained with AI may not be reproducible even within given error bounds. While reproducibility may not be needed for some uses of AI (e.g. when AI is used for the purpose of preliminary triage in drug discovery) in other uses, reproducible AI is paramount. Therefore, reproducibility is a component of trustworthiness in some cases. This minitrack will explore a number of themes related to explainable, reproducible and trustworthy AI. This includes but is not limited to the following topics:

  • Methods for explaining AI models
  • Methods to ensure reproducibility of machine learning predictions
  • Mental models for interpreting AI results
  • Methods and algorithms for detecting bias in AI/ML models
  • Approaches to perform sanity checks on data transformation pipelines
  • Approaches, tools and best practices to keep track of experiments
  • Use cases of explainability and reproducibility with AI models
  • Examples of when AI models introduce bias in results
  • Definitions and examples of trustworthy AI
Minitrack Co-Chairs:

Line Pouchard (Primary Contact)
Brookhaven National Laboratory

Peter Salholer