Salience-based stakeholder selection to maintain stakeholder coverage in solving the next release problem

Context: The quantification of stakeholders plays a fundamental role in the selection of appropriate requirements, as their judgement is a significant criterion, as not all stakeholders are equally important. The original proposals modelled stakeholder importance using a weighting approach that may not capture all the dimensions of stakeholder importance. Furthermore, actual projects involve a multitude of stakeholders, making it difficult to consider and compute all their weights. These facts lead us to search for strategies to adequately assess the importance concept, reducing the elicitation effort. Objective: We propose grouping strategies as a means of reducing the number of stakeholders to manage in requirement selection while maintaining adequate stakeholder coverage (how selection meets stakeholder demands). Methods: Our approach is based on the salience of stakeholders, defined in terms of their power, legitimacy, and urgency. Diverse strategies are applied to select important stakeholder groups. We use k-means, k-medoids, and hierarchical clustering, after deciding the number of clusters based on validation indices. Results: Each technique found a different group of important stakeholders. The number of stakeholder groups suggested experimentally (3 or 4) coincides with those indicated by the literature as definitive, dominant, dependent, and dangerous for 4 groups; or critical, major, and minor for 3 groups. Either for all the stakeholders and for each important group, several requirements selection optimisation problems are solved. The tests do not find significant differences in coverage when important stakehold- ers are filtered using clustering, regardless of the technique and number of groups, with a reduction between 66.32% and 87.75% in the number of stakeholders considered. Conclusions: Applying clustering methods to data obtained from a project is useful in identifying the group of important stakeholders. The number of suggested groups matches the stakeholders’ theory, and the stakeholder coverage values are kept in the requirement selection

Project success and critical success factors of construction projects: project practitioners’ perspectives

Project management is primarily practitioneroriented and loaded with many critical success factors (CSFs), and although these are well-evidenced in theory, they do not deliver as efficiently as factors of interest to project professionals during execution. The present study explores the perceptions of senior project managers (PMs) about project success, CSFs and complexity in large construction projects. Data from project practitioners were collected through semi-structured interviews and analysed using content analysis. The participants were selected with convenience sampling method given the complex understanding of the domain and included highly experienced PMs from the global community with expertise in project management. PMs perceive a small number of CSFs in contrast to the large exhaustive CSFs listed in the questionnaire surveys. Though important, traditional constraints of the Iron Triangle are considered inadequate in defining project success. Project professionals are seen as relying more on other performance indicators for defining a project as a success. They perceive complex construction projects in terms of a large number of interfaces, complex working systems and uncertainty. The findings of this paper suggest that project practitioners perceive differently about the CSFs and project success

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Acceptance of ambient assisted living (AAL) technologies among older Australians : a review of barriers in user experience

One of the great challenges facing Australian society is that of an ageing population. Amongst the issues involved in this drastic demographic change, the most significant aspect is the demand for older Australians to live independently at home. The development of Ambient Assisted Living (AAL) technologies aims to address this issue. The advancement of AAL applications have been done to support the users with their daily-life activities and health concerns by providing increased mobility, security, safety in emergencies, health-monitoring, improved lifestyle, and fall-detection through the use of sensors. However, the optimum uptake of these technologies among the end-users (the elderly Australians) still remains a big concern. Thus, there is an elevated need to understand the needs and preferences of the seniors in order to improve the acceptance of AAL applications. The aim of this study is to investigate the barriers and perceptions in the use of AAL applications amongst older Australians. Focus groups and quantitative surveys have been conducted to provide a detailed analysis of these impediments. The results show that there are different factors that restrict the use of these technologies along with the fact that elderly people have certain preferences when using them. An understanding of these factors has been gained and suggestions have been made to increase the acceptance of AAL devices. This work gives useful insights towards the design of AAL solutions according to user needs

COVID-19 challenges : can industry 4.0 technologies help with business continuity?

The COVID-19 pandemic has halted economic activities and made business dynamics much more challenging by introducing several additional operational, structural, and managerial constraints. The problem has affected global supply chains in many ways, and has questioned their long-term continuity. On the other hand, Industry 4.0 is an emerging phenomenon. However, there is a need to investigate how Industry 4.0 technologies may play a potential role in sustaining business operations to ease unprecedented causalities. The current research aims to investigate the potentiality of Industry 4.0 technologies to solve the COVID-19 challenges for long term sustainability. From an exploratory literature analysis coupled with the Delphi method, keeping in view the situation of the pandemic, ten challenge groups that have affected global business dynamics were identified. A questionnaire was developed with the aim of accumulating industrial and academic experts to evaluate the degree of influence and interrelationship among the identified challenges. The Decision Making, Trial and Evaluation Laboratory (DEMATEL) approach was deployed to further analyze the challenges for the categorization of these into causes and effects, further prioritizing them for better decision making. The prioritized challenges from the list of causes were governmental policies and support, followed by real access to customers and a lack of infrastructure. Additionally, these challenges were further evaluated through the expert opinion of Industry 4.0 systems experts and strategic-level supply chain experts to potentially gauge the potency of Industry 4.0 technologies to solve COVID-19-induced challenges. The outcomes of this research (which used Delphi integrated with a DEMATEL approach) are expected to support businesses in formulating strategies with the aim of business continuity in combating future disruptions caused by COVID-19-like pandemics

Local Governance in Rural Land Conflict Management

The study has attempted to assess the role of Local Governance in Rural Land Conflict Management in relation to Transparency and Accountability. The general objective of this study is to assess and understand the role and contribution of the local land governance structures and institutions to the management of the diverse rural land conflicts. Both qualitative and quantitative approaches were used for the study by making one supplementing the other. The study assessed the role of local governance in rural land conflict management by using 136 households who had experienced conflicts in the last two years. Four Kebeles with high prevalence of rural land conflicts were purposively selected from the total of 20 Kebeles of Ganta Afeshum wereda. Moreover, focus group discussion and interview were also employed to collect the qualitative data. It was found that most of the land cases are managed formally even though the preference of the litigants was the informal or the customary one as it reduces time and money, maintains the relationship of litigants, and it is believed by the rural community that the customary one is a win-win to both litigants. It is also an important means of consensus building approach. The study also showed that unclear land provision scheme is the main sources of conflicts. Boundary conflict is found as the main type which is occurred frequently. Transparency and accountability are used to measure the local land governance as indicators, and it was found that both indicators are at their minimal level. Land Governance structures and institutions at local level are not supportive enough to the rural community in terms of reaching the poor and the marginalized group. The governance structure was found weak, non-participative, biased to the rich and not inclusive as the individuals working with in the structure are incapable, inexperienced and corrupted ones. Gap in enforcement of land laws, unclear land provision schemes by the government, unclear land entitlement procedures, and low coverage of the land governance structures and institutions are the major challenges of the local land governance that possibly could result in rural land conflicts. Hence, to minimize problems related to land governance the structures and institutions should increase their coverage besides to equipping it with educated, capable, experienced and motivated manpower through providing trainings and other motivating factors like allowances, salary increments, recognition etc. This might enhance the transparency and accountability of these institutions. Customary way of conflict management should be considered in the legal conflict management systems to easily manage conflicts before cases are filed in the legal system. This reduces time, money and other resource to the community with a significant level. Hence, the livelihood of the rural community will be enhanced to a better level

Robust and Fair Machine Learning under Distribution Shift

Machine learning algorithms have been widely used in real world applications. The development of these techniques has brought huge benefits for many AI-related tasks, such as natural language processing, image classification, video analysis, and so forth. In traditional machine learning algorithms, we usually assume that the training data and test data are independently and identically distributed (iid), indicating that the model learned from the training data can be well applied to the test data with good prediction performance. However, this assumption is quite restrictive because the distribution shift can exist from the training data to the test data in many scenarios. In addition, the goal of traditional machine learning model is to maximize the prediction performance, e.g., accuracy, based on the historical training data, which may tend to make unfair predictions for some particular individual or groups. In the literature, researchers either focus on building robust machine learning models under data distribution shift or achieving fairness separately, without considering to solve them simultaneously. The goal of this dissertation is to solve the above challenging issues in fair machine learning under distribution shift. We start from building an agnostic fair framework in federated learning as the data distribution is more diversified and distribution shift exists from the training data to the test data. Then we build a robust framework to address the sample selection bias for fair classification. Next we solve the sample selection bias issue for fair regression. Finally, we propose an adversarial framework to build a personalized model in the distributed setting where the distribution shift exists between different users. In this dissertation, we conduct the following research for fair machine learning under distribution shift. • We develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution; • We propose a framework for robust and fair learning under sample selection bias; • We develop a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process; • We propose a learning framework that allows an individual user to build a personalized model in a distributed setting, where the distribution shift exists among different users

Analysis of industry 4.0 implementation in mobility sector: An integrated approach based on QFD, BWM, and stratified combined compromise solution under fuzzy environment

The role of new technologies in industrial and service sector is inevitable. Various sectors like transport / mobility have decided to remodel and redesign their infrastructures by implementing innovative devices and strategies. Transport / mobility sector is one of the most fast-growing industries which demands innovative solutions, however, it will be complex to derive optimal decision while one confront uncertain conditions and variables. In this paper, we develop a decision support system for technology adoption in transport / mobility division within the context of Industry 4.0 considering a case study in Spain. To find the adopted technology in this sector, several alternatives (options) and variables (criteria) should be assumed. We propose an integrated decision-making system including quality function deployment (QFD) and best-worst method (BWM) to find the importance weight of each criterion. After we apply the stratified Combined compromise solution (S-CoCoSo) to rate the alternatives and rank them under a multi-scenario perspective. The results will be analyzed through some sensitivity analysis actions. The novelty of our proposed decision support model contributes to the mobility sector and releases guidelines to managers and policy makers