Access control models and implementation guidelines for determining, provisioning, and de-provisioning user permissions are challenging due to the differing approaches, unique for each organization, the lack of information provided by case studies concerning the organization’s security policies, and no standard means of implementation procedures or best practices. Although there are multiple access control models, one stands out, role-based access control (RBAC). RBAC simplifies maintenance by enabling administrators to group users with similar permissions. This approach to managing user permissions supports the principle of least privilege and separation of duties, which are needed to ensure an organization maintains acceptable user access security requirements. However, if not properly maintained, RBAC produces the problem of role explosion. What happens when security administrations cannot maintain the increasing number of roles and their assigned permissions provisioned to the organization users? This paper attempts to solve this problem by implementing a scalable RBAC system and assigning each permission a risk value score determined by the severity of risk it would expose the organization to if someone had unauthorized access to that permission. Using RBAC’s role and permission design, each user will be assigned a risk value score determined by the summation of their roles’ risk based on permission values. This method allows security administrators to view the users and roles with the highest level of risk, therefore prioritizing the highest risk users and roles when maintaining user roles and permissions.

This study investigated the effects of an electronic visual activity schedule (eVAS; i.e., FIRST THEN Visual Schedule application) paired with the system of least prompts on the latency period and level of independence that a fifth-grade student with Intellectual Disability needed to transition throughout the day. Also investigated were the value that the teacher and student placed on the use of an eVAS to teach daily life and school routines instead of typical instruction (e.g., traditional visual schedule) and the student’s ability to generalize the use of the eVAS across instructors and materials (display). An ABAB single case design was used to investigate the effects of the eVAS app. Study research results indicated a functional relation between the use of the eVAS paired with the system of least prompts and the students correct responding and decreased latency. Limitations of the study and the suggestions for future research are discussed.

Mass spectrometry has been used extensively in recent years as a valuable tool in the study of proteomics. However, the data thus produced exhibits hyper-dimensionality. Reducing the dimensionality of the data often requires the imposition of many assumptions which can be harmful to subsequent analysis. The IP algorithm is a dimension reduction algorithm, similar in purpose to latent variable analysis. It is based on the principle of maximum entropy and therefore imposes a minimum number of assumptions on the data. Partial Least Squares (PLS) is an algorithm commonly used with proteomics data from mass spectrometry in order to reduce the dimension of the data. The IP algorithm and a PLS algorithm were applied to proteomics data from mass spectrometry to reduce the dimension of the data. The data came from three groups of patients, those with no tumors, malignant or benign tumors. Reduced data sets were produced from the IP algorithm and the PLS algorithm. Logistic regression models were constructed using predictor variables extracted from these data sets. The response was threefold and indicated which tumor classifications each patient belonged. Misclassification rates were determined for the IP algorithm and the PLS algorithm. The rates correct classification associated with the IP algorithm were equal or better than those rates associated with the PLS algorithm.

Sound pressure equalization of audio signals using digital signal processors has been a subject of ongoing study for many years. The traditional approach is to equalize sound at a point in a listening environment, but because of its specific dependence on the room frequency response between a source and receiver position, this equalization generally causes the spectral response to worsen significantly at other locations in the room. This work presents both a time-invariant and a time-varying implementation of an adaptive acoustic energy density equalization filter for a one-dimensional sound field. Energy density equalization addresses the aforementioned challenge and others that relate to sound equalization. The theory and real-time implementation of time-invariant sound pressure and energy density equalizers designed using the least-squares method are presented, and their performances are compared. An implementation of a time-varying energy density equalizer is also presented. Time-invariant equalization results based on real-time measurements in a plane-wave tube are presented. A sound pressure equalizer results in a nearly flat spectral magnitude at the point of equalization. However, it causes the frequencies corresponding to spatial nulls at that point to be undesirably boosted elsewhere in the sound field, where those nulls do not exist at the same frequencies. An energy density equalization filter identifies and compensates for all resonances and other global spectral effects of the tube and loudspeaker. It does not attempt to equalize the spatially varying frequency nulls caused by local pressure nodes at the point of equalization. An implementation of a time-varying energy density equalizer is also presented. This method uses the filtered-x filter update to adjust the filter coefficients in real-time to adapt to changes in the sound field. Convergence of the filter over time is demonstrated as the closed end of the tube is opened, then closed once again. Thus, the research results demonstrate that an acoustic energy density filter can be used to time-adaptively equalize global spectral anomalies of a loudspeaker and a one-dimensional sound field.

The evolution of third-party logistics has sparked much academic interest over the years, with researchers looking at how the industry has grown and the factors that have contributed to it. The primary goal of this thesis is to examine the factors that have driven third-party logistics companies’ growth in Sweden for eighteen years (2002–2020). Growth in this thesis is considered as the growth of net sales. A total of 924 observations of ninety-five active third-party logistics companies operating in the Swedish market are analyzed in this thesis. An Ordinary Least Squares (OLS) linear regression analysis is carried out to examine the growth factors of third-party logistics companies in Sweden, taking into consideration the direct effect of the variables, number of employees, liabilities, operating profit, Earnings Before Interest and Taxes (EBIT), accessibility, technology, reverse logistics, service offer, customer relationship management (CRM), marketing, and distribution systems on the growth of net sales. Results from the OLS model shows that the size of the company, profitability, and the company's level of competence, has a significant positive influence on third-party logistics (3PL) company's growth.

This thesis is, to my knowledge, the first study utilising data from the Swedish population and housing censuses between 1960 and 1990 merged with other data from the same period in order to estimate extensive margin labour supply responses to changes in municipal tax rate changes. Given that women historically have not faced the same structural labour market preconditions as men, the empirical strategy is designed to allow for an analysis of gender-based heterogeneity in labour supply responses. Using a weighted fixed effects framework, estimates of the average over time between municipal effects of tax rate increases are presented. Using the preferred main model specification, the estimate for the average tax rate elasticity is -0.165 for men and 0.3513 for women. Additionally, an attempt is made to estimate an effect using a difference-in-difference framework, treating the overall largest municipal tax rate changes as a form of quasi-experimental treatment. The results of the main analysis indicate the presence of gender-based heterogeneity in extensive margin labour supply responses during 1960 to 1990 within the administrative region in question.

Mobile applications tend to access data beyond their intended functionality and share this data with third parties for various purposes including marketing, profiling and advertisement. This data also includes user’s personal information and access to this personal information without user’s consent put user’s privacy at risk. User’s Inability to easily find privacy friendly apps and befuddling permission requests paves the way for malicious apps to get access to user’s personal information. Keeping in mind the different level of privacy aware users, we have presented a privacy enforcement framework in this thesis. This framework not only helps user to find alternative privacy friendly apps but also encourage users to review their privacy settings on the smartphone. An app discovery tool is developed to search privacy friendly apps amongst the group with similar functionality. The search results are sorted by privacy friendly score which is calculated using simplified version of risk assessment method known as EBIOS. Threat posed to personal information by various apps are then highlighted and presented to user in an easy-to-understand way before installing the app. We have validated the results of our discovery tool by comparing them to the manual inspection of various functional groups i.e., group of applications with similar functionality. Two list of permissions, one created by subjective and manual analysis of abstract functionality of functional group called expert opinion and other created by our tool based on permissions requested by functional group are compared. Our tool has correctly identified the permissions which are similar to expert opinion. / Mobila applikationer tenderar att ta del av data utanför deras tilltänkta funktionalitet och delar den här datan med tredjehands parter för olika syften som marknadsföring, profilering och reklam. Datan inkluderar även personlig information och tillgång till den personliga informationen utan användarens medvetande sätter användarens integritet i risk. Användares oförmåga att enkelt hitta integritetsvänliga appar och förvirrande godkännande förfrågningar öppnar vägen för illvilliga appar att få tillgång till användarens personliga information. Med tanke på hur olika användare uppmärksammar integritetnivåer presenterar vi ett integritetsupprätthållande ramverk i den här uppsatsen. Ramverket hjälper inte bara användare att hitta integritetsvänliga appar utan uppmuntrar även användaren att granska integritetsinställningarna i sin telefon. Ett applikationsupptäckarverktyg utvecklades för att söka efter integritetsvänliga appar inom samma funktionsområde. Sökresultatet är sorterat efter en integritetsvänlighetspoäng beräknad med en förenklad version av riskbedömningsmetoden känd som EBIOS. Hot mot personlig information från olika appar uppmärksammas och presenteras på ett användarvänligt sätt innan appen installeras. Vi har validerat resultatet från vårt applikationsupptäckarverktyg genom att jämföra det med en manuell inspektion av appar inom samma funktionsområde, exempelvist grupper av applikationer med liknande funktion. Två listor togs fram, en framtagen genom subjektiv och manuell analys av normal funktionalitet kallad expertutlåtande och en framtagen av vårt applikationsupptäckarverktyg baserat på funktionsområde. Vårt verktyg har korrekt identifierat godkännande i likhet med expertutlåtandet.

It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention. In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity. A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length. In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition. In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks' adaptability, resulting in the disrupted dominance of small-world brain networks.

This dissertation addressed the need for empirical research on the leadership of multinational virtual teams (Davis & Bryant, 2003; Early & Gibson, 2002; Ilgen, Lepine, and Hollenbeck, 1997; Prieto & Arias, 1997), particularly in the field of educational leadership (Cheng, 1995). This was accomplished through the development of a model based upon the cultural values discovered through the use of Hofstede's (1980, 2001) Values Survey Module 94 (VSM 94). As workers will bring values from their own cultures to the multinational workplace (Bochner & Hesketh, 1994), research was conducted to determine the cultural values of economics professors in the United States and Germany in order to formulate a cultural contingent leadership model based on Triandis' (1993) adaptation of Fiedler's (1967) contingency theory. Given a total response from 194 U.S. and German economics professors, it was discovered that faculty in both the United States and Germany had values that differed significantly from those that Hofstede discovered for his IBM employee samples in four out of five dimensions. However, it was found that the values for the U.S. and German faculties were a close replication of Hoppe's (1990) findings which were based on a sample that was similar to the economics faculties in both occupation and education. These findings add a cautionary note to the recommendation by Hofstede and Peterson (2000) that existing cultural values can be used by cultural researchers: Previous cultural value data can be used if the samples are closely matched to the previous samples in both nationality as well as educational and occupational background. The research thus indicated that differences in national culture, as measured by Hofstede's (1980, 2001) cultural dimensions, still exist. In addition, a direct comparison of the cultural values between the two faculties indicated that the U.S. and German economics faculties differed significantly in two of Hofstede's cultural dimensions, individualism and collectivism and masculinity and femininity. The two samples were not significantly different in the cultural dimensions of power distance, uncertainty avoidance, and long term orientation. Using the Triandis (1993) approach, application of the research results to Fiedler's (1967) LPC model indicated that a relationship oriented leader would be an in-match leader for a group of U.S. and German higher education economics faculty. This result was contingent upon the cultural values discovered for the U.S. and German faculties who would be involved in an endeavor with situational variables similar to that which would be found in an international joint venture to offer online distance economics education to students in a developing country.

Cloud computing is becoming increasingly popular among all types of companies due to its inherent benefits. However, because of its infrastructure, it might be difficult to manage access rights between users and resources. To address these difficulties, Amazon Web Services (AWS) provides Identity and Access Management (IAM) and features that support the use of different access control models, for example, Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC). Access control models are used for authorisation within systems to decide who gets access to what. Therefore, to determine what constitutes an efficient (the average time it takes to perform a task in AWS IAM) and secure access control model, a thorough study of background material and related work was conducted. Through this study, it was found that RBAC lacked scalability whilst ABAC lacked administrative capabilities. It was also found that flexibility and scalability were two important factors when designing access control models. Furthermore, by conducting a survey and designing an access control model for AWS through various iterations, a new access control model called Entity-centric Attribute- & Role-based Access Control (EARBAC) was developed. In an experiment comparing it with the RBAC model, the EARBAC model was found to be both efficient and secure, in addition to its flexibility and scalability. Furthermore, EARBAC was also found to be 27% faster than RBAC in AWS IAM. These results suggest that the model is useful when developing cloud infrastructures in AWS.