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The American Institute of Certified Public Accountants and the Accounting Education Change Commission have mandated the adoption of 150 semester hours for accounting students and as a prerequisite for taking the CPA examination. More than 40 states have already adopted the 150‐hour requirement. Proponents of the change have argued that accounting education has to change from a knowledge‐based education to a process‐oriented programme and develop a process of inquiry and a desire for life‐long learning in the students. They hope to effect changes to result in improved intellectual, communication, and interpersonal skills, and a better understanding of the broad picture in a business. It is argued here that although the mandate may have been well‐intentioned but knowingly or unknowingly the authors have chosen to ignore the most essential component, i.e. what does it take to teach the students for success in the accounting profession? It is argued that the missing link is primarily trained and versatile teachers, followed with the lacking prerequisites for the students and their parents. Furthermore, no consideration has been given to the additional cost involved for the students and their parents and the question of commensurate job opportunities for those involved.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

Social networking platforms are increasingly using the Follower Link Prediction tool in an effort to expand the number of their users. It facilitates the discovery of previously unidentified individuals and can be employed to determine the relationships among the nodes in a social network. On the other hand, social site firms use follower–followee link prediction (FFLP) to increase their user base. FFLP can help identify unfamiliar people and determine node-to-node links in a social network. Choosing the appropriate person to follow becomes crucial as the number of users increases. A hybrid model employing the Ensemble Learning algorithm for FFLP (HMELA) is proposed to advise the formation of new follower links in large networks.

HMELA includes fundamental classification techniques for treating link prediction as a binary classification problem. The data sets are represented using a variety of machine-learning-friendly hybrid graph features. The HMELA is evaluated using six real-world social network data sets.

The first set of experiments used exploratory data analysis on a di-graph to produce a balanced matrix. The second set of experiments compared the benchmark and hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments show that the proposed (HMELA) method predicts missing links better than other methods.

A hybrid suggested model for link prediction is proposed in this paper. The suggested HMELA model makes use of AUC scores to predict new future links. The proposed approach facilitates comprehension and insight into the domain of link prediction. This work is almost entirely aimed at academics, practitioners, and those involved in the field of social networks, etc. Also, the model is quite effective in the field of product recommendation and in recommending a new friend and user on social networks.

The outcome on six benchmark data sets revealed that when the HMELA strategy had been applied to all of the selected data sets, the area under the curve (AUC) scores were greater than when individual techniques were applied to the same data sets. Using the HMELA technique, the maximum AUC score in the Facebook data set has been increased by 10.3 per cent from 0.8449 to 0.9479. There has also been an 8.53 per cent increase in the accuracy of the Net Science, Karate Club and USAir databases. As a result, the HMELA strategy outperforms every other strategy tested in the study.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

How can the situation of shoppers staying at home and being unable to experience malls prepare retailers for the new marketplace? The COVID-19 lockdown provides a unique opportunity to examine the value of mall experiences to shoppers. This study aims to suggest a new mall experiences loss (MEXLOSS) model for assessing the importance of mall experiences as the foundation of any future strategy for attracting shoppers back to the mall.

A total of 498 British shoppers completed an online survey during the May 2020 COVID-19 pandemic lockdown.

When the exchanges of resources manifested in mall experiences are absent, the perceived difficulty of substituting an experience increases shoppers’ longing for the experience, which in turn increases both willingness to pay and mall loyalty but decreases well-being. Using a conceptualization of four types of mall experiences, i.e. functional, seductive, recreational and social, the functional and recreational experiences are shown to be the most valuable.

In the new more careful service marketplace, shoppers’ preferences are increasingly oriented toward health, safety, sustainability, collaboration and digitalization. To improve their resilience and attractiveness, malls need to adjust their layout, retail mix, digitalization, activities and connectivity according to these trends and to the characteristics of each mall experience.

To the best of the authors’ knowledge, this study is the first to place a financial value on mall experiences and to use the absence of those experiences to assess their general and relative importance. The findings challenge previous assumptions about the superiority of online shopping and the decreasing attractiveness of malls.

The purpose of this paper is to explore the links between forced marriage, running away/going missing and child sexual exploitation.

An extensive research review and interviews with experts and practitioners across the three fields identified a total of 22 cases in which young people (aged 18 and under) had experienced some combination of all three issues. Of these, nine case studies involving South Asian young people were explored in depth using a case study methodology.

Through adopting constitutive intersectionality as an analytical framework, the power of “community” emerged as a distinct theme within the cases. Concern about both family and community “honour” impacted young people’s decision making and help seeking processes. “Honour” also impacted parental responses to the young people as well as how they engaged with the professionals seeking to support them.

The safety of mothers also emerged as an issue, suggesting that this is an area for further research.

Practical implications for practice included: the need to address barriers to young people disclosing abuse and entering into the criminal justice process; difficulties associated with finding safe spaces to work with young people; the need to identify effective ways of working with abused young people who are unable to draw on relational and social support; and dangers associated with accessing support services.

An extensive review of the relevant research literature failed to uncover links between forced marriage, going missing and child sexual exploitation. This led the author to assert that the risk of child sexual exploitation as it relates to young South Asian young people who run away from home to escape forced marriage has been both under-acknowledged and under-explored (Sharp, 2013). Empirical research undertaken by the author over a 15-month period confirmed this assertion.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

In recent years, there have been calls on African countries to develop disaster governance system that recognises the complex nature of disaster events and reflects how their impact can prevent governments from achieving their development objectives. This chapter examines Ghana’s response to the calls by exhuming disaster management policies and the missing links in their implementation. This research was approached by comprehensively reviewing literature related to the subject. The review was supported by field-based interviews involving key stakeholders, some of whom are directly involved with disaster policy planning and implementation. The results show that even though there are several disaster management policies, they are fragmented and found within several institutions, a situation that has created some missing links in their implementation. For instance, poor collaboration and coordination among disaster management institutions hampered efforts towards disaster risk prevention, preparedness and mitigation which are viewed as cross-cutting themes in disaster management. Even more intriguing, critical stakeholders such as community groups who often play important roles in rescue and recovery operations and continue to render humanitarian services after official operations have ended are excluded from the everyday decision-making processes. Empirically, this chapter draws attention to how endogenous interventions that are deeply rooted in the culture of the people that could support well-crafted disaster legislations are often ignored. This chapter concludes that these missing links need to be addressed in order to make Ghana resilient to disaster.

The purpose of this systematic literature review is to understand what the phenomenon of uncaptured value is, identify where it is operationalized and explore how it can be transformed into value opportunities. Uncaptured value in sustainable business model innovation can lead to new value creation which, in turn, can promote practices of innovation, sustainability and inclusiveness.

A systematic literature review was conducted using eight databases to identify 47 articles using the phrase sustainable business model innovation along with the terms value uncaptured, value surplus, value absence, value missed and value destroyed.

The findings have identified that uncaptured value is reoccurring in sustainable business model innovation but is left as the missing link. This paper outlines the novelties of uncaptured value in sustainable business model innovation into a framework that can be used for future research, which is also discussed, concluded and suggested.

A framework for the continued research on uncaptured value in sustainable business model innovation with an emphasis on influences, operationalization and practices has been created to further the research frontier and capture the missing link.

The purpose of this paper is to know the rate of loss of online citations used as references in scholarly journals. It also indented to recover the vanished online citations using Wayback Machine and also to calculate the half-life period of online citations.

The study selected three journals published by Emerald publication. All 389 articles published in these three scholarly journals were selected. A total of 15,211 citations were extracted of which 13,281 were print citations and only 1,930 were online citations. The online citations so extracted were then tested to determine whether they were active or missing on the Web. W3C Link Checker was used to check the existence of online citations. The online citations which got HTTP error message while testing for its accessibility were then entered in to the search box of the Wayback Machine to recover vanished online citations.

Study found that only 12.69 percent (1,930 out of 15,211) citations were online citations and the percentage of online citations varied from a low of 9.41 in the year 2011 to high of 17.52 in the year 2009. Another notable finding of the research was that 30.98 percent of online citations were not accessible (vanished) and remaining 69.02 percent of online citations were still accessible (active). The HTTP 404 error message – “page not found” was the overwhelming message encountered and represented 62.98 percent of all HTTP error message. It was found that the Wayback Machine had archived only 48.33 percent of the vanished web pages, leaving 51.67 percent still unavailable. The half-life of online citations was increased from 5.40 years to 11.73 years after recovering the vanished online citations.

This is a systematic and in-depth study on recovery of vanished online citations cited in journals articles spanning a period of five years. The findings of the study will be helpful to researchers, authors, publishers, and editorial staff to recover vanishing online citations using Wayback Machine.