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This paper provides an introduction to research in the field of image forensics and asks whether advances in the field of algorithm development and digital forensics will facilitate the examination of images in the scientific publication process in the near future.

This study looks at the status quo of image analysis in the peer review process and evaluates selected articles from the field of Digital Image and Signal Processing that have addressed the discovery of copy-move, cut-paste and erase-fill manipulations.

The article focuses on forensic research and shows that, despite numerous efforts, there is still no applicable tool for the automated detection of image manipulation. Nonetheless, the status quo for examining images in scientific publications remains visual inspection and will likely remain so for the foreseeable future. This study summarizes aspects that make automated detection of image manipulation difficult from a forensic research perspective.

Results of this study underscore the need for a conceptual reconsideration of the problems involving image manipulation with a view toward the need for interdisciplinary collaboration in conjunction with library and information science (LIS) expertise on information integrity.

This study not only identifies a number of conceptual challenges but also suggests areas of action that the scientific community can address in the future.

Image manipulation is often discussed in isolation as a technical challenge. This study takes a more holistic view of the topic and demonstrates the necessity for a multidisciplinary approach.

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.

This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.

The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.

The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

Presents an overview of automated fabric flaw detection immediately after the knitting process. Considers the classification of fabric flaws and how image processing techniques can be applied to their classification, via an introductory example. Outlines problems associated with automating this inspection process and discusses possible flaw sensing systems and techniques.

Development of non‐destructive methodology for detection of arc strike, spatter and fusion type of welding defects which may form on steam generator (SG) tubes that are in close proximity to the circumferential shell welds. Such defects, especially fusion‐type defects, are detrimental to the structural integrity of the SG. This paper aims to focus on this problem.

This paper presents a new methodology for non‐destructive detection of arc strike, spatter and fusion type of welding defects. This methodology uses remote field eddy current (RFEC) ultrasonic non‐destructive techniques and K‐means clustering.

Distinctly different RFEC signals have been observed for the three types of defects and this information has been effectively utilized for automated identification of weld fusion which produces two back‐wall echoes in ultrasonic A‐scan signals. The methodology can readily distinguish fusion‐type defect from arc strike and spatter type of defects.

The methodology is unique as there is no standard guideline for non‐destructive evaluation of peripheral tubes after shell welding to detect arc strike, spatter and fusion type of welding defects.

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps.

The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application.

The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks.

This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world.

To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

In recent decades, professionals in the architecture, engineering and construction industry have come to recognize building information modeling (BIM) as one of the most powerful technologies available to ensure successful project outcomes. The purpose of this paper is to explore the benefits of BIM on design defect prevention during the design phase of building projects.

The authors qualitatively analyzed 160 design defect leading indicators (LIs) to identify key themes for design defect prevention. Then, by matching appropriate BIM functionalities to each key LI theme, they identified BIM-supported key LI themes.

The result of this paper served as the foundation of a BIM-based key design processes framework, which identifies the necessary data, project parties, actions and applicable BIM functions for preventing particular design defects. In addition, the authors found that BIM implementation can benefit 71.2% of the LIs of the design defects associated with problematic deliverables.

This study establishes the current state of BIM use for design defect prevention and also gives practitioners precisely targeted guidelines for using BIM functions during the design phase for better quality management.

Automating the task of identifying process weaknesses using process models is promising, as many organizations have to manage a large amount of process models. The purpose of this paper is to introduce a pattern-based approach for automatically detecting potential process weaknesses in semantic process models, thus supporting the task of business process improvement.

Based on design research, combined with a case study, the authors explore the design, application and evaluation of a pattern-based process weakness detection approach within the setting of a real-life case study in a German bank.

Business process weakness detection can be automated to a remarkable extent using pattern matching and a semantic business process modeling language. A case study provided evidence that such an approach highly supports business process analysts.

The presented approach is limited by the fact that not every potential process weakness detected by pattern matching is really a weakness but just gives the impression to be one. Hence, after detecting a weakness, analysts still have to decide on its authenticity.

Applying weakness patterns to semantic process models via pattern matching allows organizations to automatically and efficiently identify process improvement potentials. Hence, this research helps to avoid time- and resource-consuming manual analysis of process model landscapes.

The approach is not restricted to a single modeling language. Furthermore, by applying the pattern matching approach to a semantic modeling language, the authors avoid ambiguous search results. A case study proves the usefulness of the approach.

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

Many economic, political and socio-cultural events in the 2020s have been strong headwinds for architecture, engineering and construction (AEC). Nevertheless, technological advancements (e.g. artificial intelligence (AI), big data and robotics) provide promising avenues for the development of AEC. This study aims to map the state of the literature on automation in AEC and thereby be of value not only to those researching automation and its composition of a variety of distinct technological and system classes within AEC, but also to practitioners and policymakers in shaping the future of AEC.

This review adopts scientometric methods, which have been effective in the research of large intra and interdisciplinary domains in the past decades. The full dataset consists of 1,871 articles on automation in AEC.

This overarching scientometric review offers three interdisciplinary streams of research: technological frontiers, project monitoring and applied research in AEC. To support the scientometric analysis, the authors offer a critical integrative review of the literature to proffer a multilevel, multistage framework of automation in AEC, which demonstrates an abundance of technological paradigm discussions and the inherent need for a holistic managerial approach to automation in AEC.

The authors underline employee well-being, business sustainability and social growth outcomes of automation and provide several managerial implications, such as the strategic management approach, ethical management view and human resource management perspective. In doing so, the authors seek to respond to the Sustainable Development Goals proposed by the United Nations as this becomes more prevalent for the industry and all levels of society in general.

This study aims to undertake a contextual analysis of project-level building information modelling (BIM) adoption in Nigeria and demonstrate how BIM is applied across different projects in Nigeria.

This research generates contextual and holistic understandings of multiple project-level cases of BIM adoption through an interpretive paradigm guided by relativist ontology and subjectivist epistemology. Two models of project-level BIM adoption (ten-factor theory of BIM adoption and strategic-contingent model of BIM adoption) were merged to formulate the BIM adoption assessment scale (BIM-AAS). A qualitative-oriented case study protocol was developed to extract valid and reliable data from external and internal project data based on BIM-AAS features. The extracted data were analysed using the pattern-matching technique and cross-case analysis.

The results indicate that there was substantial use of BIM tools and technologies in the projects. All the projects adopted collaborative procurement and team and developed integrated building information models. The use of BIM tools, technologies and processes in the projects was found to be above average. The complexities and expectations levels of the projects compliment the nature of BIM adoption in the projects.

The BIM-AAS adopted in this research is an excellent example of a project-level BIM adoption analytical tool. It can be assumed in future research. Also, this research contributes to the theory that the level of project complexity and expectations must align with the level of BIM adoption in projects. The study’s findings ratify BIM tools, technologies and processes as the elements of project-level BIM adoption.

This research substantiates the actual nature and structure of BIM adoption in Nigeria, thereby simplifying the development of initiatives towards BIM adoption in projects and determining the appropriate strategies for BIM implementation and innovation in the Nigerian construction industry. The most important initiative that the Nigerian government can make to drive BIM implementation is the automating of code checking for building rules and regulations in Nigeria.

Previous studies have only reported cases of project-level BIM adoption using surveys and without a standardised project-level BIM adoption model to guide the analysis. This study is the first to formulate and use BIM adoption models for a uniform, critical and contextual analysis of project-level BIM adoption.