E-government is a critical strategic initiative that governments worldwide are rapidly adopting to enhance public service delivery. The successful implementation of e-services depends on the engagement of citizens and the private sector in digital transformation efforts. The Jordanian government, particularly the Greater Amman Municipality (GAM), is committed to advancing e-government; however, change management among senior public officials remains a significant challenge. This study evaluates change management strategies and leadership practices in GAM’s e-government services, assessing their effectiveness in addressing transformation challenges. A qualitative research approach was employed, utilizing secondary sources (published reports, prior studies) and primary sources (interviews with GAM senior management). The findings reveal both strengths and obstacles in GAM’s e-transformation efforts. While mayoral support facilitated progress, a lack of clear sustainability and accountability measures remains a challenge. To address these gaps, the study proposes a conceptual framework for effective change management, highlighting the critical role of leadership, internal and external barriers, and sustainability concerns in e-government projects. This framework serves as a strategic guide for policymakers and organizations, contributing to improved digital governance and facilitating smoother public sector transitions to e-government. Keywords: E-government, digital transformation, change management, leadership, public sector innovation.

Addressing the under-researched issue of weapon tolerance, the paper examines factors behind male knife and gun tolerance across four different cultures, seeking to rank them in terms of predictive power and shed light on relations between them. To this end, four regression and structural equation modelling analyses were conducted using samples from the US (n = 189), India (n = 196), England (n = 107) and Poland (n = 375). Each sample of male participants indicated their standing on several dimensions (i.e., predictors) derived from theory and related research (i.e., Psychoticism, Need for Respect, Aggressive Masculinity, Belief in Social Mobility and Doubt in Authority). All four regression models were statistically significant. The knife tolerance predictors were: Aggressive Masculinity (positive) in the US, Poland and England, Belief in Social Mobility (negative) in the US and England, Need for Respect (positive) in India and Psychoticism (positive) in Poland. The gun tolerance predictors were: Psychoticism (positive) in the US, India and Poland, Aggressive Masculinity (positive) in the US, England and Poland, and Belief in in Social Mobility (negative) in the US, Belief in Social Mobility (positive) and Doubt in Authority (negative) in Poland. The Structural Equation Weapon Tolerance Model (WTM) suggested an indirect effect for the latent factor Perceived Social Ecological Constraints via its positive relation with the latent factor Saving Face, both knife and gun tolerance were predicted by Psychoticism.

This thesis investigated and explored the challenges in accessing Bangladeshi women to talk about their experiences of domestic violence. The aim of the thesis had been to consider. a) Whether and how the voices of Bangladeshi women are heard within the context of domestic violence and abuse and whether women see themselves as victims of abuse. b) The challenges of hearing Bangladeshi children’s voices and whether mothers see their children as victims of abuse in the home. c) Factors that help or hinder Bangladeshi women and their children sharing their experiences of domestic violence and abuse. My research explored the impact and challenges of the societal, cultural, and community perceptions and the diversity of women’s experiences, through the lens of intersectionality and the positionality of the women. I heard directly from eight women about their lived experiences of different forms of abuse. This took place through face-to-face and online semi-structured interviews conducted in English and Bangla, using a framework to collect data through interviews, video, audio recordings and note taking processes. Accessing women for interviews was difficult as they were fearful of speaking about violence in the home and the repercussions this may bring for them and their wider families. This represented a break from what is considered as a societal norm for Bangladeshi women. There were challenges in the subsequent analysis in terms of translating and transcribing interviews from Bangla to English without compromising on the validity and originality of the women’s own expression of their experiences. I interviewed ten professionals within two focus groups who were mainly from a social care setting. I sought their views on working with Bangladeshi women experiencing domestic violence and abuse and what in their views, helped or hindered women of Bangladeshi origin seeking support from their agencies. The complexity of individuals experiences, their fears, and the community responses highlighted how the women perceive that they deserve to remain in an abusive relationship. The key findings of the research include the understanding and the importance of marriage within the Bangladeshi community and the role of the extended family as highly significant, both of which in turn limits women’s ability and willingness to seek support. It considered the strength women gain from their faith and giving forgiveness of their abusers as a way of healing themselves, and how they blame themselves for the abuse they have suffered. The thesis focused on the barriers and challenges they faced while they navigate the lack of understanding of their multiple identities of being a woman of colour cultural norms, language, and religious belief. The lack of recognition of abuse in a community which is patriarchal is a major factor in women not seeking, or finding, the support they need. The thesis highlighted the role of the extended family and the community pressures that force women to remain in an abusive relationship for much longer they wanted to. Women spoke about leaving being seen as a sense of individual failure and not protecting the name and honour of the family regardless of the women’s emotional, financial, or physical standing in the community. They highlighted how they want to protect the wider family in the UK and abroad, often by remaining in a marriage and suffering the abuse. I also argue that the social structure and inequalities created significant boundaries and limitations for these women, where undue responsibilities were placed on them for being the protector of children and saving the honour of the community and society at large as a woman. Lastly, I conclude my research and discussed how women see the lack of support from agencies and how this collides and intersects with in the choices they make about the abuse they experienced, and how their decisions to remain or leave an abusive relationship are based on who they are as a Bangladeshi woman and how they are seen within the wider society. My recommendations include the need to ensure Bangladeshi women’s experiences of domestic violence and abuse in research become more visible in research, which will require understanding and commitment on the part of researchers. Larger scale and longitudinal research is required that does not begin with the assumption that the community is problematic. Women must have a more prominent voice and opportunities to challenge current policies that do not recognise their specific needs or encourage and support them. This needs to happen through having others like them talking and involving women in support services nationally and locally and, most, within the wider social and faith community. I recommend that women should be able to speak about abuse without threats of alienation and retribution affecting their wider families. I recommend that there need to be government guidelines on destigmatising domestic violence and abuse within the faith community in having joint work tackling domestic violence and abuse. There needs to be more connection across services, including an acceptance that religious beliefs and practices can be used positively to support women victims of domestic violence and abuse. This will also require support through statutory guidance and policies that are accessible to the Bangladeshi community and help open discussion about domestic violence and abuse within the community in a way that is not associated with shame and dishonour.

This thesis examines how creative practice contributes to a heightened portrayal of the emotional history of survivor and victim families, following the sinking of a Commissioners of the Irish Lights Tender, SS Isolda on 19th December 1940 by the Luftwaffe, off the coast of Wexford, Ireland. Six men died and their bodies were never found. Due to wartime censorship and Irish neutrality, the sinking was not reported in the media for five years. The emotional and practical impact of that silence and absence is examined, within a framework of faith, remembrance and grief. The thesis explains why not writing creatively to recreate the actual sinking is a more powerful way to show themes of absence and loss. Including factual reports, photographs, letters and newspaper articles, contributes to a polyphony of voices, intended to add veracity to the creative work. It explores the ethnographic, examining cultural tensions and class hierarchies of Emergency Ireland, the self-reflectiveness of metafiction and how the speculative combines to reanimate stories of the crew and their widows. There is a reflection on both ambiguous loss – the loss where there is no body and on postmemory, as drivers to explain why these stories remain important to families intergenerationally, even if the family stories can be unreliable. There is an examination of the liminality of faction/creative non-fiction and of a cumulative short story structure as an alternative to a linear novel.

In SLAM, weak environmental information refers to the lack of indications of distinctive landmarks in sensor data. This brings difficulties to SLAM as it relies on the indications. Various approaches of dealing with weak environmental information have been reported. Single-robot-based ones require either large datasets (for running machine learning) or artificial landmarks both of which need the prior knowledge of the environments where robots explore. Multiple-robots-based/swarm-based approaches seem more promising as they rely on information sharing among robots rather than the prior knowledge. They are more robust when facing the failure of individual robots. In the collaborative swarm-based SLAM, individual robots run their own SLAM at local level and contribute to the development of a global map and to the estimation of their locations in the global map. The key to success is the collaboration among the robots. The existing collaboration mechanisms leave SLAM at both local and global levels completely to individual robots, leading to inconsistent global maps. They also require individual robots to have sufficient computational power. This research develops a novel collaborative swarm robot system tailored for global SLAM in weak environmental information scenarios. The system introduces four key advancements: (1) designing a coordination strategy to guide robots toward unexplored regions, improving exploration efficiency and coverage; (2) implementing cross-validation mechanisms to identify and eliminate redundant landmarks from local maps, ensuring data accuracy; (3) enabling individual robots to extend their perception by leveraging peer robots' sensory data, allowing relative localization and integration of local maps in sparse environments; and (4) creating a centralized global map framework to consolidate data from all swarm members, ensuring consistency in the overall mapping process.

In today’s world, financial institutions rely massively on Internet banking to efficiently make services available to customers. Due to the increase in credit card use, the number of frauds has also escalated. Research has been done using state-of-the-art techniques to effectively avoid credit card fraud. The proportion of fraudulent and legit transaction in financial (credit card) data is unbalanced, and Data produced per second from the banking system are substantial in number, diversified, and heterogeneous. Therefore, the conventional methods of fraud detection have been proven to be challenging to tackle all the challenges. Adapting machine learning techniques to credit card fraud detection reduces many obstacles, as illustrated in the literature. Although machine learning techniques are efficient in credit card fraud detection, there are some additional challenges researchers must face, the imbalanced data is one of them. The researchers proposed collecting large amounts of data to balance the data distribution. One of the ways to address the issue of imbalance is to share data from different institutions or organisations. But due to Different regulation, the challenge is the data sharing by banks and financial institutes to access to the continuous stream of real-time data to learn the new heterogeneous patterns. The research was conducted to understand how data can be collect from different institutions without breaking different regulations like GDPR, or Data Protection Act 2018 in a privacy-preserving manner. This study introduces a framework that merges federated learning with other machine learning algorithms to improve credit card fraud detection, while also maintaining the privacy of data across the collaborating institutions. In the research analysis, different machine learning models such as random forest (RF), logistic regression (LR), multilayer perceptron (MLP), artificial neural networks (ANN), support vector machine (SVM), and decision tree (DT), were used. Along with these classical models, the research also experimented with the combination of Federated learning framework. The research also analysed the model's performance with and without the combination where it’s been found that ANN, random forest and XGboost performed well. The contribution of the research is a combined approach to integrating the machine learning model with the decentralised approach of the federated learning framework. In this approach, data are trained locally and only the trained model from the individual device is combined on a central server while keeping data privacy. The secure aggregation encryption method used by the FL framework ensures the end-to-end model transmission privacy between the clients and the server, thus following GDPR. The future scope of the contributed framework will allow banks and financial institutes to share the data collaboratively to train the machine learning model for effective CCFD. The initial experiment and results show that the performance of the artificial neural network, random forest and XGboost performed well in combination with federated learning provides better results than the other machine learning algorithms.

Milton Keynes was designated as a New Town in 1967 with a population of around 60,000 (Milton Keynes Council, 2011), and was originally allocated for housing and employment. For the last 30 years it has been one of the fastest growing United Kingdom cities in terms of economic output and employment, resulting in labour productivity being higher than the national average and with more growth forecast ahead. The global and United Kingdom economy will be changing, and many of these changes and disruptors cannot be anticipated. The purpose of this research study is to make sure Milton Keynes is equipped for current and changing needs through exploring, in a real-world setting, interpretations by senior managers of the links between investment in skills and the contribution to enhanced organisational and macro-economic performance. With a particular focus on Milton Keynes, employer attitudes and behaviour towards developing a skilled workforce will be investigated in order to enhance understanding and knowledge of the skills element of human capital and understand better the correlation between these interdependent components. In order to do this, the study focuses on answering four key questions: 1. To what extent is there a problem in Milton Keynes businesses with recruitment related to skills and training? 2. Do employers in Milton Keynes understand the links between investing in skills and training with business performance? 3. What are the attitudes and behaviours of employers in developing a skilled workforce? 4. What role does human capital play alongside technology for employers? This was achieved through assessing current local, national, and international reports and literature. In addition, real-time insight provided a summation of what is actually happening on the ground, through primary data collected through a business survey; methodologies employed include a questionnaire (sample size 1428 Milton Keynes organisations), as well as focus group methods using quantitative and qualitative approaches. Also included were 15 one-to-one interviews with businesses in Milton Keynes. All three methods provided a rich amount of data and insight from direct sources. This research was carried out as there was not enough evidence or reports in existence which looked at Milton Keynes in isolation, how it is performing and why. The contribution made to knowledge through the undertaking of this study has exposed a lot of direct information and findings from Milton Keynes businesses using different methods. A study in this detail over this period of time has never been undertaken before and therefore provides a unique insight, with rich information upon which to make recommendations and form conclusions directly relevant to Milton Keynes. This work provides a unique contribution to policy development and challenges theories such as that of Becker (1964) and Jacob’s (1961, 1969), suggesting that they should be revised to encapsulate other factors that play just as significant a role in business performance as education and skills development. This thesis reveals that factors such as macro-regional location, entrepreneurial activity and local infrastructure play a significant role in attracting highly skilled individuals, as opposed to the mere presence of a higher education institution. Therefore, a revised theory may wish to revisit the link between higher education institutions in a town or city and their link with business performance and productivity. Interestingly, employers in Milton Keynes do appear to acknowledge skills gaps. However, while those skills gaps may prevent businesses from reaching their full potential, it does not appear to hinder productivity. One of the most prominent findings of this thesis is that employers seem to only seek to take action to rectify a skills gap once the problem does look to affect productivity, otherwise employers were previously aware of this issue but not taking action. Another interesting finding is that in the focus group many employers often spoke about the need to attract skilled professionals, as opposed to investing in training and development or education. Through a combination of the literature and the results produced by primary research, one could argue that the construction of a higher education institution in Milton Keynes may not be necessary to equip Milton Keynes with sustainable skills, nor will it improve business performance. However, with employers dissatisfied with the local skills base and frequently considering the problem to stem from individuals “not having the right skills”, as well as a large number of employers who do not have any formal links with education institutions in Milton Keynes – it would be wise for employers to consider building closer partnerships with schools and education institutions. That way, they could have more of an influence on the skills being taught in schools as well as growing the local skills base that are equipped with the “right” skills to succeed in and contribute to the local economy. This rich source and analysis of data provides insights into a roadmap and strategic direction that policy makers in Milton Keynes could exploit in order to identify better the issues highlighted in this thesis work. This presents the opportunity for Milton Keynes to lead the way in terms of strategic planning in relation to the skills agenda, and create a future plan, based on this work which can be an exemplar of best practice. It is planned that this report will be circulated to several organisations with direct influence on the ability of Milton Keynes to deliver solutions which will help businesses to grow and flourish and resolve the skills gaps emerging from the research. One of these will be the central Government departments responsible for funding of skills, education and for helping businesses

To take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.

To face the new challenge of giving an all-around picture of people's online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how users post their favorite contents and about their centrality on different social media. Specifically posting activities and social sites usage have been gathered from the social media aggregator Alternion. The analysis of social media usage shows that Alternion data capture the typical trend of today's users. However the novelty is the multidimensional and longitudinal nature of the dataset. In fact by performing a rank correlation analysis on the degree in the different social sites, we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals' importance changes from medium to medium.We also investigate the posting activities finding a slightly positive correlation on how often users publish on different social media. Finally we show that users tend to use similar usernames to keep their identifiability across social sites.

The continual launches of new online social media that meet the most varied people’s needs are resulting in a simultaneous adoption of different social platforms. As a consequence people are pushed to handle their identity across multiple platforms. However, due the to specialization of the services, people’s identity and behavior are often partial, incomplete and scattered in different “places”. To overcome this identity fragmentation and to give an all-around picture of people’s online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how and when users post their favorite contents, about their centrality on different social media and about the choice of their username. Specifically we gathered the posting activities and social sites usage from Alternion, a social media aggregator. The analysis of social media usage shows that Alternion data reflect the novel trend of today’s users of branching out into different social platforms. However the novelty is the multidimensional and longitudinal nature of the dataset. Having at our disposal users’ degree in five different social networks, we performed a rank correlation analysis on users’ degree centrality and we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals’ importance changes from medium to medium. The longitudinal nature of the dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Finally we show that users tend to use similar usernames to keep their identifiability across social sites.