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The purpose of this paper is to focus on the development of an integrated computerised maintenance management system to improve the information storage of design and construction, diagnostic and defect risk assessments on IBS building through the integration of building information modelling (BIM).

The methodology used interviews with the IBS building client/maintenance contractor in Malaysia to gather information about maintenance management problems, approaches to address problems, information and communication technology implementation and use of emerging technologies, in addition to prototyping a system development life cycle for system development.

Relevant process flowchart documents of system development were obtained from the case study and reviewed to assist in providing an automation technique for decision-making and structural defect diagnostic operation through the integration of Visual Basic.Net, MS Access and Autodesk Revit software.

This research focuses on automatic bidirectional communications between an Expert System and BIM on a database level. Adoption of the approaches suggested in the research will enable the system to promote the development of zero IBS building maintenance.

This paper aims at improving progress tracking and control of repetitive projects by developing a novel framework that automates the documentation of as-built information directly into the project schedule and also introduces enhanced linear scheduling formulation to support project control decisions.

The proposed framework uses e-mail technology to facilitate detailed tracking of daily as-built events of all parties through bidirectional communication between site and head office. It also provides a new formulation for more accurate critical path and linear scheduling computation to accurately update the project's time and cost during construction.

Using a case study of a road project, the paper proves that the proposed framework reduces as-built documentation effort and its schedule updates are more responsive to all as-built events than traditional scheduling techniques.

The proposed method applies to linear projects (e.g. highways) and can be extended to other repetitive projects such as high-rise buildings. It can also be extended to include voice features and procedures for forensic schedule analysis.

The developed methodology presents a low-cost approach to document timely progress information for decision makers of massive linear projects (often associated with infrastructure) to have better control over the execution of projects, save documentation time and cost, and avoid disputes and problems.

This research contributes in improving construction productivity by collecting timely as-built information using affordable communication technologies. It also presents novel advancements to the existing scheduling and control techniques to suit linear projects, which are most challenging.

The current maintenance management method affects the efficiency of the complex, high-rise industrialised building system (IBS) building maintenance management projects in Malaysia. The usage of a conventional system (paper-based reports/unsystematic database) has various drawbacks, such as IBS component aesthetic and structural defects which occur repeatedly in the building and maintenance of conventional building, lack of integration between maintenance systems and lack of the intelligent capabilities of linking defect diagnosis operations in maintenance affecting various building elements with IBS component defect knowledge. Thus, the purpose of this paper is to focus on the development of a mechanism to improve IBS component defect knowledge transfer in IBS building maintenance projects through the integration of building information modelling (BIM).

Eight clients/maintenance contractors were selected based on the major problems of using a conventional system to investigate the maintenance management practices in each complex and high-rise IBS building.

The findings indicated that several computerised systems such as Building Automation System and Supervisory Control and Data Acquisition System are used by a client/maintenance contractor. Moreover, BIM technology awareness was found to be limited, with no implementation in IBS building maintenance till now. The results of this case study were used as a foundation for the development of a prototype system using Computerised Maintenance Management System, Expert System and BIM.

The development of this new system improves IBS component diagnosis knowledge integration with the BIM technology.

Supply chains are quite easy to define for manufacturing organizations where each participant in the chain receives inputs from a set of suppliers, processes those inputs, and delivers them to a distinct set of customers. With service organizations, one of the primary suppliers of process inputs is customers themselves, who provide their bodies, minds, belongings, or information as inputs to the service processes. We refer to this concept of customers being suppliers as “customer‐supplier duality.” The duality implies that service supply chains are bidirectional, which is that production flows in both directions. This article explores the customer‐supplier duality as it pertains to supply chain management, including practical and managerial implications.

This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

The purpose of this paper is to propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job-seeker qualifications and skills, against the vacancy provided by employers or job-agents.

The paper presents a framework of bidirectional jobseeker-to-vacancy matching system. Using occupational data from various sources such as the WageIndicator web survey, International Standard Classification of Occupations, European Skills, Competences, Qualifications, and Occupations as well as vacancy data from various open access internet sources and job seekers information from social networking sites, the authors apply machine learning techniques for bidirectional matching of job vacancies and occupational standards to enhance the contents of job vacancies and job seekers profiles. The authors also apply bidirectional matching of job seeker profiles and vacancies, i.e., semantic matching vacancies to job seekers and vice versa in the individual level. Moreover, data from occupational standards and social networks were utilized to enhance the relevance (i.e. degree of similarity) of job vacancies and job seekers, respectively.

The paper provides empirical insights of increase in job vacancy advertisements on the selected jobs – Internet of Things – with respect to other job vacancies, and identifies the evolution of job profiles and its effect on job vacancies announcements in the era of Industry 4.0. In addition, the paper shows the gap between job seeker interests and available jobs in the selected job area.

Due to limited data about jobseekers, the research results may not guarantee high quality of recommendation and maturity of matching results. Therefore, further research is required to test if the proposed system works for other domains as well as more diverse data sets.

The paper demonstrates how online jobseeker-to-vacancy matching can be improved by use of semantic technology and the integration of occupational standards, web survey data, and social networking data into user profile collection and matching.

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the model. This study adds a layer based on attention mechanism (AM), which captures the context vector and gives keywords higher weight for text classification. Second, this study explains the authors’ model’s results with local interpretable model-agnostic explanations (LIME).

Bloom's taxonomy levels of cognition are commonly used as a reference standard for identifying e-learning contents. Many action verbs in Bloom's taxonomy, however, overlap at different levels of the hierarchy, causing uncertainty regarding the cognitive level expected. Some studies have looked into the cognitive classification of e-learning content but none has looked into learning objectives. On the other hand, most of these research papers just adopt classical machine learning algorithms. The main constraint of this study is the availability of annotated learning objectives data sets. This study managed to build a data set of 2,400 learning objectives, but this size remains limited.

This study’s experiments show that the proposed model achieves highest scores of accuracy: 90.62%, F1-score and loss. The proposed model succeeds in classifying learning objectives, which contain ambiguous verb from the Bloom’s taxonomy action verbs, while the same model without the attention layer fails. This study’s LIME explainer aids in visualizing the most essential features of the text, which contributes to justifying the final classification.

In this study, the main objective is to propose a model that outperforms the baseline models for learning objectives classification based on the six cognitive levels of Bloom's taxonomy. In this sense, this study builds the bidirectional GRU (BiGRU)-attention model based on the combination of the BiGRU algorithm with the AM. This study feeds the architecture with word2vec embeddings. To prove the effectiveness of the proposed model, this study compares it with four classical machine learning algorithms that are widely used for the cognitive classification of text: Bayes naive, logistic regression, support vector machine and K-nearest neighbors and with GRU. The main constraint related to this study is the absence of annotated data; there is no annotated learning objective data set based on Bloom’s taxonomy's cognitive levels. To overcome this problem, this study seemed to have no choice but to build the data set.

Over the past two decades, scholarly attention has focused mainly on a direct and inverse relationship between corporate environmental responsibility (CER) and corporate financial performance (CFP). This study aims to explore the bidirectional causality hypothesis, as good environmental results can lead to good financial results, which makes it possible to invest more resources in projects that improve environmental performance.

The authors test the bidirectional causality between CER and CFP on a sample of listed Italian manufacturing firms over the 2005-2014 period. The authors use a fixed effect panel data regression and check the robustness of the results with alternative econometric techniques.

Although the findings do not support bidirectional hypothesis, they establish direction/causality from CFP to CER. As a result, environmental responsibility is a consequence of prior financial performance, which supports the slack resources hypothesis.

Given that companies’ environmental commitment is dictated by economic evaluations or by assessing the availability of resources to invest, it seems that the spread of environmentally responsible behaviours might be supported by different external pressures.

The paper provides further insights on sustainability management literature by establishing a bidirectional relationship between firm performance and environmental responsibility.

To solve the above problems and ensure the stability of the ad hoc network node topology in the process of wireless signal transmission, this paper aims to design a robust adaptive sliding film fault-tolerant controller under the nonlinear distortion of signal transmission in an amorphous flat air-to-ground wireless ad hoc network system.

This paper designs a robust adaptive sliding film fault-tolerant controller under the nonlinear distortion of signal transmission in an amorphous flat air-to-ground wireless ad hoc network system.

The simulation results show that the amorphous flat wireless self-organizing network system has good nonlinear distortion fault-tolerant correction ability under the feedback control of the designed controller, and the system has the asymptotically stable convergence ability; the test results show: the node topology of the self-organizing network structural stability is significantly improved, which provides a foundation for the subsequent realization of long-distance transmission of ad hoc network nodes.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The controller can extract the fault information caused by nonlinear distortion in the wireless signal transmission process, and at the same time, its feedback matrix K can gradually converge the generated wireless signal error to zero, to realize the stable transmission of the wireless signal.