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Numerous challenges in education emerge as our technology-driven society rapidly evolves and manifests more exigent requirements from engineering professionals. Higher education, nonetheless, seems to adapt to such requirements at an unequal speed, generating some tensions between industry and higher education institutions. The purpose of this paper is to share the experiences obtained through a process of assessment and redesign of a large enrollment course of programming from which the authors developed a systematic approach for course design/redesign.

A mixed method approach was deployed for data gathering and evaluation, consisting of close-ended surveys, open-ended questionnaires, information matrices and state of the art compilation. Triangulation of the information offered clear data about the necessity of curriculum redesign; therefore, a new programming course curriculum encompassed with relevant necessities in engineering and science was developed.

The authors produced a coherent and dynamic systematic path for assessment and design/redesign of course curriculum, which the authors find extremely helpful to improve negotiation processes inside higher education institutions, as it can be implemented to improve any large enrollment course curricula in engineering and science.

By following the systematic path for assessment and design/redesign of curricula the authors developed, higher education systems could embark more efficiently in the ever-challenging process of adapt their courses and programs to tackle the upcoming demands of our society.

So far, a systematic path for assessment and design/redesign of course curriculum was not published, and it supports the improvement of pedagogical approaches in academic institutions.

In this work, the authors propose to harness the advantages of behavioral programming as a new technique for designing rule-based conversational agents.

To examine the study’s hypotheses, the authors perform a first-of-its-kind user study through which the authors examine how potential designers, both expert designers, computationally-oriented designers, and otherwise, leverage behavioral programming (BP) and dialog graphs for designing conversational agents (CAs). The authors also use two standard CA settings common in the literature: designing a CA representative for a user in an online dating service and a non-character player in a role-playing game (RPG).

The study’s results indicate that BP can be successfully utilized by computationally-oriented designers, with or without prior knowledge in CA design, and can facilitate the design of better CAs (i.e. more accurate and more robust). However, to capitalize on these potential advantages, designers may be required to devote more time to the design process and are likely to encounter higher temporal demand levels. These results suggest that BP, which was initially proposed and evaluated in the general context of software design, can constitute a valuable alternative to the classic rule-based CA design technique commonly practiced today.

An important limitation of this study is the relatively small participant pool. While the authors do plan to extend this study in the future, the current coronavirus disease 2019 (COVID-19) situation makes it ever more complex to conduct formal user studies of this kind. It is, however, important to note that despite the low number of participants, many of the results are found to be statistically significant.

The authors plan to continue this line of work and conduct human studies for additional design techniques in other popular agent-based settings. Specifically, the authors seek to explore how people of different backgrounds should design agents for various tasks such as automated negotiation (e.g. how should a person design a representative agent to negotiate on her behalf?) and social choice (e.g. how should a person design a voting bot to represent her in online voting systems?).

People are increasingly interacting with conversational agents in various settings and for a variety of reasons, as the market size of those agents keeps on growing every year. Through a first-of-its-kind human study (N = 41), consisting of both expert designers, computationally-oriented designers, and otherwise, the authors demonstrate a few key advantages and limitations of BP in the realm of conversational agents and propose its consideration as an alternative to the classic dialog graph technique.

In a technological era driven by coding, programming and artificial intelligence (AI), there is more need than ever to develop computing skills and knowledge for non-specialist students. Nonetheless, the literature on computer science teaching methods of non-scientific majors is not as comprehensive as that of scientific ones.

Pedagogically, the authors designed and implemented prototyping from John Dewey's pragmatic epistemological lens. Using a mixed methods approach, the authors tested the effectiveness and efficiency of this approach within the same course over four semesters across four academic years.

As an epistemological pedagogic device, prototyping facilitated learning by doing and experimenting and stimulated graduate students' self-directed learning, engagement and their overall ownership of the learning and teaching process, changing their role from being merely passive recipients of “strange, awkward and unfamiliar” knowledge to active constructors of “relevant and exciting” content knowledge. Such a change was reflected in the significant progress students made, driven by their commitment, motivation and enthusiasm, irrespective of their prior knowledge and age (Generations X, Y and Z). Prototyping also served as an avenue for a “Deweyian Reflection”, where graduate students, after internalizing the acquired computing skills and knowledge, started transferring such skills and knowledge to their professions (journalism and public relations (PR)) and daily practices.

Findings from this study will add to the literature review on this subject matter and will inform future case studies in computer science education for graduate students from non-scientific backgrounds.

This paper reveals that learning by doing/experimenting needs to be accompanied by in-depth reflection to enable students to transfer the acquired knowledge and skills to other settings and contexts beyond that of the lesson, task, or project in hand.

There is little research published on introductory programming courses offered to non-specialized students (i.e. students from non-scientific backgrounds). This study contributes to the body of research on how to effectively engage these students in programming courses informed by John Dewey's pragmatic lens/epistemological lens.

The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).

This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.

To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.

A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.

The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.

In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

Smart contracts using blockchain technology (BCT) is a tool that decentralizes authority and makes it easier to upgrade the contract administration process by providing an efficient system. Current literature provides a good overview of contracts in the construction industry; however, the specific details of BCT's smart contracts applications in the three categories have not been addressed adequately: (1) information quality, (2) enhancing project schedule and progress payment time and (3) reducing conflicts among project stakeholders. Thus, this study aims to analyze smart contracts using BCT by creating a computerized contract model, specifically evaluating its impact on the three identified categories.

In this paper BCT-SmContract was developed through an automated program that utilizes blockchain to define the contractual agreements between different parties in a construction project. BCT-SmContract model provides a new technique to overcome the current challenges associated with factors identified in this study, i.e. (1) information quality, (2) enhancing project schedule and progress payment time and (3) reducing conflicts among project stakeholders. Afterward, the model was tested to ensure validity and reliability through a construction project.

The findings indicated that BCT-SmContract was approximately 90% faster to execute the contract and 100% accurate in reflecting the correct information about the project status, resulting in reduced conflicts.

This study has contributed in upgrading the traditional contracting method in construction by developing an automated smart contract model to enhance the processes and achieve higher accuracy.

This study aims to apply an open-source approach to protect the 3D printing industry from innovation stagnation due to broad patenting of obvious materials.

To do this, first an open-source implementation of the first five conditions of an open-source algorithm developed to identify all obvious 3-D printing materials was implemented in Python, and the compound combinations of two and three constituents were tested on ten natural and synthetic compounds. The time complexity for combinations composed of two constituents and three constituents is determined to be O(n²) and O(n³), respectively.

Generating all combinations of materials available on the Chemical Abstracts Services (CAS) registry on the fastest processor on the market will require at least 73.9 h for the latter, but as the number of constituents increases the time needed becomes prohibitive (e.g. 3 constituents is 1.65 million years). To demonstrate how machine learning (ML) could help prioritize both theoretical as well as experimental efforts a three-part biomaterial consisting of water, agar and glycerin was used as a case study. A decision tree model is trained with the experimental data and is used to fill in missing physical properties, including Young's modulus and yield strength, with 84.9 and 85.1% accuracy, respectively.

The results are promising for an open-source system that can theoretically generate all possible combinations of materials for 3-D printing that can then be used to identify suitable printing material for specific business cases based on desired material properties.

Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts.

Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results.

The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally.

Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience.

Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost.

Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams.

This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.

The first purpose is to assess the quality of containerized multimodal export and the second is to develop and demonstrate the design of a service network with quality approach.

The article used the structural equation model to develop a model to measure the quality of multimodal transportation for containerized exports and finalized the model with an alternative approach. The evolutionary algorithm had been used to design a service network based on quality.

Provided factors affecting quality of multimodal transportation and reverse to one hypothesis, the construct variation in cost, time shape and quantity did not affect the quality of multimodal transportation for containerized exports. The model without variation construct was finalized by exploring causality.

This research had scope till container loading onto the vessel and assessed the quality for containerized cargo only, and second research purpose is limited by assumed values of fitness function and the limited number of nodes, in service network design demonstration.

This research provided a tool to measure the quality of multimodal transportation for containerized exports and demonstrated the field application of the model developed in service network design. This approach included all factors applicable across the container movement. The integrated approach of the article provided an organized method to design a service network for containerized exports.

This work provided the tool to assess the quality of multimodal transportation for containerized exports and developed an approach to design a service network of multimodal transportation based on quality. This approach has considered the factors of multimodal transportation comprehensively in contrast to the optimization approaches based on operation research techniques.

The purpose of this paper is to present a verification methodology for custom micro coded components designed for Avionics projects. Every electronic hardware which will be developed for an aircraft must be designed with the compliance of DO-254 processes. Requirements are the key elements of the aviation. All the requirements must be covered by the design to be considered as completed. Therefore, verification of the custom micro coded components against requirements should be comprehensively addressed. The verification using the manual testing approach is less preferable, as humans can possibly make mistakes. Therefore, the most used verification method today is the automated simulation.

The industry has developed a common methodology for generating automated testbenches by following the standardized guideline. This methodology is named as the universal verification methodology (UVM). In this paper, the verification study of ARINC-429 data bus digital design is presented to describe the DO-254 verification process using the UVM.

The results are supported with functional coverage and code coverage in addition to the assertions. It is observed that the design worked correctly.

To the best of the authors’ knowledge, this is the first study comprehensively describing the DO-254 verification process and demonstrating it by the UVM application of ARINC-429 on programmable logic devices.

This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals.

Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId).

This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data.

From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.