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  • Public defence: 2019-09-10 12:30 J1620, Karlskrona
    Ahmadi Mehri, Vida
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Towards Secure Collaborative AI Service Chains2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    At present, Artificial Intelligence (AI) systems have been adopted in many different domains such as healthcare, robotics, automotive, telecommunication systems, security, and finance for integrating intelligence in their services and applications. The intelligent personal assistant such as Siri and Alexa are examples of AI systems making an impact on our daily lives. Since many AI systems are data-driven systems, they require large volumes of data for training and validation, advanced algorithms, computing power and storage in their development process. Collaboration in the AI development process (AI engineering process) will reduce cost and time for the AI applications in the market. However, collaboration introduces the concern of privacy and piracy of intellectual properties, which can be caused by the actors who collaborate in the engineering process.  This work investigates the non-functional requirements, such as privacy and security, for enabling collaboration in AI service chains. It proposes an architectural design approach for collaborative AI engineering and explores the concept of the pipeline (service chain) for chaining AI functions. In order to enable controlled collaboration between AI artefacts in a pipeline, this work makes use of virtualisation technology to define and implement Virtual Premises (VPs), which act as protection wrappers for AI pipelines. A VP is a virtual policy enforcement point for a pipeline and requires access permission and authenticity for each element in a pipeline before the pipeline can be used.  Furthermore, the proposed architecture is evaluated in use-case approach that enables quick detection of design flaw during the initial stage of implementation. To evaluate the security level and compliance with security requirements, threat modeling was used to identify potential threats and vulnerabilities of the system and analyses their possible effects. The output of threat modeling was used to define countermeasure to threats related to unauthorised access and execution of AI artefacts.

  • Public defence: 2019-09-11 09:00 Karlskrona
    Ouriques, Raquel
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Understanding and Supporting Knowledge Management in Agile Software Development2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

     Background. Agile Software Development (ASD) promises agility and flexibility in dealing with uncertainty by prioritizing interaction between people supported by informal communication and knowledge sharing. The lack of practices to manage the knowledge as a resource might jeopardize the application of knowledge in the production of goods and service. The utilization of Knowledge Management (KM) strategies can significantly support achieving and sustaining competitive advantage and brings several benefits to software development. However, how to manage knowledge in ASD is still not well understood or investigated.

     Objectives. The main objective of this thesis is to contribute to the software engineering field by providing a different perspective on directions that KM can take to improve knowledge-based resource (KBR) management in ASD. The detailed objectives are: (i) Understand the current ASD environment regarding KM; (ii) Identify KBRs in ASD and its implications for KM; and (iii) Provide an initial set of variables to evaluate knowledge criticality of knowledge items in ASD.

     Method. We used a mixed-methods approach to address the objective of this thesis. The methods selected to conduct the studies include systematic literature review, grounded theory, and improvement case study. The data collection comprised a literature review, semi-structured interviews, and practitioners’ feedback through static validation.

     Results. From our SLR we observed that that KM strategies in ASD promote mainly knowledge transfer through practices that stimulate social interaction to share tacit knowledge in the project layer, increasing the risk of losing knowledge by keeping the knowledge localized inside a few individual’s minds. When it comes to coordination, practitioners utilize KBRs in their routines, through social collaboration within teams’ environment/settings. However, this process is nonsystematic, which brings inefficiency to KBR utilization resulting in knowledge loss. It can generate negative implications to the course of the software development, including meaningless searches in databases, frustration because of recurrent problems, and unawareness of knowledge sources. To support decision making related to knowledge retention, we have developed an initial version of the method to evaluate the criticality (KCEM) of a knowledge item, which is divided into two categories, relevance, and scarcity.

     Conclusion. The current results of this thesis are of particular interest. However, we recognize that the work is unfinished. As a complement to this thesis, we have planned our long-term objective, which is to contribute to creating scalable KM solutions for companies adopting ASD.We divide this long-term objective into three studies: Carry out a complementary study to apply KCEM in different companies; explore efficient ways of storing codified knowledge in combination with the KCEM, and investigate how to define metrics to evaluate the outcomes of KM practices.

  • Public defence: 2019-09-09 13:00 J1640, Karlskrona
    Josyula, Sai Prashanth
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Parallel algorithms for real-time railway rescheduling2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In railway traffic systems, it is essential to achieve a high punctuality to satisfy the goals of the involved stakeholders. Thus, whenever disturbances occur, it is important to effectively reschedule trains while considering the perspectives of various stakeholders. The train rescheduling problem is a complex task to solve, both from a practical and a computational perspective. From the latter perspective, a reason for the complexity is that the rescheduling solution(s) of interest may be dispersed across a large solution space. This space needs to be navigated fast while avoiding portions leading to undesirable solutions and exploring portions leading to potentially desirable solutions. The use of parallel computing enables such a fast navigation of the search tree. Though competitive algorithmic approaches for train rescheduling are a widespread topic of research, limited research has been conducted to explore the opportunities and challenges in parallelizing them.

    This thesis presents research studies on how trains can be effectively rescheduled while considering the perspectives of passengers along with that of other stakeholders. Parallel computing is employed, with the aim of advancing knowledge about parallel algorithms for solving the problem under consideration.

    The presented research contributes with parallel algorithms that reschedule a train timetable during disturbances and studies the incorporation of passenger perspectives during rescheduling. Results show that the use of parallel algorithms for train rescheduling improves the speed of solution space navigation and the quality of the obtained solution(s) within the computational time limit.

    This thesis consists of an introduction and overview of the work, followed by four research papers which present: (1) A literature review of studies that propose and apply computational support for train rescheduling with a passenger-oriented objective; (2) A parallel heuristic algorithm to solve the train rescheduling problem on a multi-core parallel architecture; (3) A conflict detection module for train rescheduling, which performs its computations on a graphics processing unit; and (4) A redesigned parallel algorithm that considers multiple objectives while rescheduling.

  • Josyula, Sai Prashanth
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Törnquist Krasemann, Johanna
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Exploring the Potential of GPU Computing in Train Rescheduling2019In: Proceedings of the 8th International Conference on Railway Operations Modelling and Analysis, Norrköping, 2019., 2019Conference paper (Refereed)
  • Johnsson, Mikael
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Swenningsson, Kristina
    Crearum AB, SWE.
    Svensson, Ewa
    Crearum AB, SWE.
    Problems when creating innovation teams2019Conference paper (Refereed)
    Abstract [en]

    This research explores problems occurring when practitioners use a research-based methodology regarding how to create high-performing innovation teams, namely the CIT-process, which has not been used by practitioners before. The CIT-process is recommended to be used prior to the ideation phase, which otherwise is considered to be the first phase in the innovation process. The CIT-process is a five-step process in which the innovation project kick-off is the final step. Prior steps refer to management commitment and identification and to the preparation of a convener and team members. A consultancy firm was educated and evaluated before organisations were involved, who brought real innovation projects to work on. Three innovation teams were created. Data were collected through observations at team meetings and interviews. Any problems were mainly caused by management that underestimated the value of preparation in innovation work. Scepticism towards the newly developed CIT-process and the practitioners' inexperience with the CIT-process were also addressed as reasons. The need for educational tools was highlighted. Further research is suggested.

  • Bertoni, Marco
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    WORK INTEGRATION SOCIAL ENTERPRISES: A TESTBED FOR CHALLENGE-BASED LEARNING?2019In: Proceedings of the 15th international CDIO Conference, Aarhus university , 2019Conference paper (Refereed)
    Abstract [en]

    Work Integration Social Enterprises (WISE) are a particular enterprise form permeated by a so-called ‘double business idea’. Besides the commercial imperative of providing product and services, WISE offer employment and training opportunities for individuals considered less able to compete in mainstream labour markets. The paper argues that this multiple goal structure makes WISE an ideal testbed for Challenge-Based Learning (CBL). The latter deepens both problem-based learning and CDIO, by featuring open-ended problems that stress an entrepreneurial, value-driven and sustainable approach to problem formulation and decision-making. The aim of this paper is to describe how real-life design projects conducted in collaboration with WISE take CBL a step forward compared with those involving more ‘traditional’ enterprise forms. Evidence is gathered along 4 main lines of thought, which are: 1) iterative problem formulating and designing; 2) entrepreneurial mindset and of value-driven learning; 3) social sustainability-aware designing; and 4) social-constructed learning. The findings indicate that WISE-based design experiences bring forward additional characteristics compared with more ‘traditional’ engineering ones. Students are able to expand the scope and depth of their problem identification and formulation activities, due to the continuous dialogue with a broad range of stakeholders, enthusiasts, and volunteers. They become more aware of the multifaceted meaning of the word ‘value’ in engineering, realizing the existence of competing value systems for the design problem. Eventually their decision-making activities emphasizes the pursuit of different goals and objectives (e.g., technical feasibility, business viability, and sustainable development) in the design process.