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Today’s software engineers often work in teams to develop complex software systems. Therefore, successful software engineering in practice require team members to possess not only sound programming skills such as analysis, design, coding and testing but also soft skills such as communication, collaboration and self-management. However, existing examination-based assessments are often inadequate for quantifying students’ soft skill development. The purpose of this paper is to explore alternative ways for assessing software engineering students’ skills through a data-driven approach.

In this paper, the exploratory data analysis approach is adopted. Leveraging the proposed online agile project management tool – Human-centred Agile Software Engineering (HASE), a study was conducted involving 21 Scrum teams consisting of over 100 undergraduate software engineering students in multi-week coursework projects in 2014.

During this study, students performed close to 170,000 software engineering activities logged by HASE. By analysing the collected activity trajectory data set, the authors demonstrate the potential for this new research direction to enable software engineering educators to have a quantifiable way of understanding their students’ skill development, and take a proactive approach in helping them improve their programming and soft skills.

To the best of the authors’ knowledge, there has yet to be published previous studies using software engineering activity data to assess software engineers’ skills.

The purpose of this paper is to propose a concept of cloud auction robot (CAR) and its execution platform for transforming perishable food supply chain management. A new paradigm of goods-to-person auction execution model is proposed based on CARs. This paradigm can shift the management of traditional manual working to automated execution with great space and time saving. A scalable CAR-enabled execution system (CARES) is presented to manage logistics workflows, tasks and behavior of CAR-Agents in handling the real-time events and associated data.

An Internet of Things enabled auction environment is designed. The robot is used to pick up and deliver the auction products and commends are given to the robot in real-time. CARES architecture is proposed while integrating three core services from auction workflow management, auction task management, to auction execution control. A system prototype was developed to show its execution through physical emulations and experiments.

The CARES could well schedule the tasks for each robot to minimize their waiting time. The total execution time is reduced by 33 percent on average. Space utilization for each auction studio is improved by about 50 percent per day.

The CAR-enabled execution model and system is simulated and verified in a ubiquitous auction environment so as to upgrade the perishable food supply chain management into a new level which is automated and real-time. The proposed system is flexible to cope with different auction scenarios, such as different auction mechanisms and processes, with high reconfigurability and scalability.

With the rapid growth of the Internet of Things (IoT) market and requirement, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, Narrow Band IoT (NB-IoT) and long range (LoRa) are two main leading competitive technologies. Compared with NB-IoT networks, which are mainly built and managed by mobile network operators, LoRa wide area networks (LoRaWAN) are mainly operated by private companies or organizations, which suggests two issues: trust of the private network operators and lack of network coverage. This study aims to propose a conceptual architecture design of a blockchain built-in solution for LoRaWAN network servers to solve these two issues for LoRaWAN IoT solution.

The study proposed modeling, model analysis and architecture design.

The proposed solution uses the blockchain technology to build an open, trusted, decentralized and tamper-proof system, which provides the indisputable mechanism to verify that the data of a transaction has existed at a specific time in the network.

To the best of our knowledge, this is the first work that integrates blockchain technology and LoRaWAN IoT technology.

This paper aims to adapt and computerize the Trail Making Test (TMT) to support long-term self-assessment of cognitive abilities.

The authors propose a divide-and-combine (DAC) approach for generating different instances of TMT that can be used in repeated assessments with nearly no discernible practice effects. In the DAC approach, partial trails are generated separately in different layers and then combined to form a complete TMT trail.

The proposed approach was implemented in a computerized test application called iTMT. A pilot study was conducted to evaluate iTMT. The results show that the instances of TMT generated by the DAC approach had an adequate level of difficulty. iTMT also achieved a stronger construct validity, higher test–retest reliability and significantly reduced practice effects than existing computerized tests.

The preliminary results suggest that iTMT is suitable for long-term monitoring of cognitive abilities. By supporting self-assessment, iTMT also can help to crowdsource the assessment processes, which need to be administered by healthcare professionals conventionally, to the patients themselves.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

Interactions are fundamental for successful relationships and stable cooperation in a business-to-business market. The main assumption in research on interactions, so obvious that usually not stated by researchers, is that they are set between humans. The development of artificial intelligence forces the re-examination of this assumption. This paper aims to conceptualize business virtual assistants (BVAs), a type of intelligent agent, as either a boundary object or an actor within business interactions.

Reference is made to the literature on business interactions, boundary objects and identity attribution to problematize the process of interpretation through which BVA obtains an identity. The ARA model and the model of interaction process is used to create a theoretical framework.

This paper contributes to the literature on business interactions, and to the core of the IMP discussion, in three aspects. The first provides a framework to understand the phenomenon of an artificial entity as an interlocutor in business interactions. While doing that a new type of entity, BVA, is introduced. The second contribution is the exploration and augmentation of the concept of a business actor. The third calls attention to BVA as a boundary object. These issues are seen as essential to move forward the discussion about the meaning of business interaction in the near future.

This paper conceptualizes the presence of a new entity – BVA – in the business landscape.

Mixed-initiative interaction is a naturally-occurring feature of human-human interactions. It is characterised by turn-taking, frequent change of focus, agenda and control among the "speakers". This human-based mixed-initiative interaction can be implemented through mixed-initiative systems. This is a popular approach to building intelligent systems that can collaborate naturally and effectively with people. Mixed-initiative systems exhibit various degrees of involvement with regards to the initiatives taken by the user or the system. In any discourse, the initiative may be shared between either, a learner and a system agent, or between two independent system agents. Both the parties in question establish and maintain a common goal and context, and proceed with an interaction mechanism involving initiative taking that optimises their progress towards the goal. However, the application of mixed-initiative interaction in web-based learning is very much limited. This paper discusses the design and implementation of a web-based learning system through mixedinitiative system known as JavaLearn. JavaLearn allows the interaction between the system (in the form of a software agent) and the individual learner. Here, the system supports the learning through a problem solving activity by demanding active learning behaviour from the learner with minimal natural language understanding by the agent and embodies the application-dependent aspects of the discourse. It guides the learner to solve the problem by giving adaptive advice, hints and engages the learner in the real time interaction in the form of "conversation". The principal features of this system are it is adaptive and is based on reflection, observation and relation. The system acquires its intelligence through the finite state machine and rule-based agents.

The importance of artificial intelligence in human resource management has grown substantially. Previous literature discusses the advantages of AI implementation at a workplace and its various consequences, often hostile, for employees. However, there is little empirical research on the topic. The authors address this gap by studying if individuals oppose biased algorithm recommendations regarding disciplinary actions in an organisation.

The authors conducted an exploratory experiment in which the authors evaluated 76 subjects over a set of 5 scenarios in which a biased algorithm gave strict recommendations regarding disciplinary actions at a workplace.

The authors’ results suggest that biased suggestions from intelligent agents can influence individuals who make disciplinary decisions.

The authors’ results contribute to the ongoing debate on applying AI solutions to HR problems. The authors demonstrate that biased algorithms may substantially change how employees are treated and show that human conformity towards intelligent decision support systems is broader than expected.

The authors’ paper is among the first to show that people may accept recommendations that provoke moral dilemmas, bring adverse outcomes, or harm employees. The authors introduce the problem of “algorithmic conformism” and discuss its consequences for HRM.

This modeling facilitates the determination of control responses (or possibly reconfiguration) upon such events and the identification of which segments of the pipeline can continue to function uninterrupted. Based on this modeling, an algorithm is presented to implement the control responses and to establish this determination. In this work, the authors propose using Message Queuing Telemetry Transport (MQTT), which is an integrated method to perform the system-wide control based on message exchanging among local node controllers (agents) and the global controller (broker).

Complex manufacturing lines in industrial plants are designed to accomplish an overall task in an incremental mode. This typically consists of a sequence of smaller tasks organized as cascaded processing nodes with local controls, which must be coordinated and aided by a system-wide (global) controller. This work presents a logic modeling technique for such pipelines and a method for using its logic to determine the consequent effects of events where a node halts/fails on the overall operation.

The method uses a protocol for establishing communication of node events and the algorithm to determine the consequences of node events in order to produce global control directives, which are communicated back to node controllers over MQTT. The algorithm is simulated using a complex manufacturing line with arbitrary events to illustrate the sequence of events and the agents–broker message exchanging.

This approach (MQTT) is a relatively new concept in Cyber-Physical Systems. The proposed example of feed-forward is not new; however, for illustration purposes, it was suggested that a feed-forward be used. Future works will consider practical examples that are at the core of the manufacturing processes.

Children suffering from cancer or cardiovascular disease, who need extended periods of treatment in hospitals, are subjected to multiple hardships apart from the physical implications, for example, experienced isolation and disrupted social and academic development. This has negative effects long after the child's recovery from the illness. The purpose of this paper is to examine the non-medical needs of children suffering from a long-term illness, as well as research the field of artificial intelligence (AI) – more specifically, the use of socially intelligent agents (SIAs) – in order to study how technology can enhance children's interaction, participation and quality of life.

Interviews were performed with experts in three fields: housing manager for hospitalized children, a professor in computing science and researcher in AI, and an engineer and developer at a tech company.

It is important for children to be able to take control of the narrative by using an SIA to support the documentation of their period of illness, for example. This could serve as a way of processing emotions, documenting educational development or keeping a reference for later in life. The findings also show that the societal benefits of AI include automating mundane tasks and recognizing patterns.

The originality of this study concerns the holistic approach of increasing the knowledge and understanding of these children's specific needs and challenges, particularly regarding their participation and interaction with teachers and friends at school, using an SIA.