A conditional role-involved purpose-based access control model

This paper presents a role-involved conditional purpose-based access control (RCPBAC) model, where a purpose is defined as the intension of data accesses or usages. RCPBAC allows users using some data for certain purpose with conditions. The structure of RCPBAC model is defined and investigated. An algorithm is developed to achieve the compliance computation between access purposes (related to data access) and intended purposes (related to data objects) and is illustrated with role-based access control (RBAC) to support RCPBAC. According to this model, more information from data providers can be extracted while at the same time assuring privacy that maximizes the usability of consumers' data. It extends traditional access control models to a further coverage of privacy preserving in data mining environment as RBAC is one of the most popular approach towards access control to achieve database security and available in database management systems. The structure helps enterprises to circulate clear privacy promise, to collect and manage user preferences and consent

Distribution of future location vector and residual sum of squares for multivariate location-scale model with spherically contoured errors

The multivariate location-scale model with a family of spherically contoured errors is considered for both realized and future responses. The predictive distributions of the future location vector (FLV) and future residual sum of squares (FRSS) for the future responses are obtained. Conditional on the realized responses, the FLV follows a multivariate Student-t distribution whose shape parameter depends on the sample size and the dimension of the location parameters of the model, and the FRSS follows a scaled beta distribution. The results obtained by both the classical and Bayesian methods under uniform prior are identical. This paper generalizes the results for location-scale models with multivariate normal and Student-t models to a wider family of spherically/ellipticcally contoured models

The Moderating Influence of the Strength of Racial Identity on the Relationship Between Teacher-Student Racial Similarity-Dissimilarity and Classroom Engagement

This research, titled ‘The moderating influence of the strength of racial identity on the relationship between teacher-student racial similarity-dissimilarity and classroom engagement’, was conducted by Md Enamul Kabir, a graduate student in the Department of Communication Studies at Minnesota State University, Mankato as a requirement for completing a Master of Arts degree in August 2020. The purpose of this quantitative study was to understand how the strength of racial identity moderates the effects of the teacher-student racial similarity and dissimilarity on the engaging behavior of students with their instructors in United States classrooms. This study questioned the prevalent assumption that similarity and dissimilarity predicted the nature of interaction and established the following primary hypothesis: the effect of similarity and dissimilarity in racial identity between teacher and students on the level of classroom engagement will depend on the students’ strength of social identification with race. 114 students participated in an online survey which was administered through Qualtrics. The results showed that the moderating effect was significant, but there was not enough evidence to support the effect at high and low levels of identification

ADC 2010: 21st Australasian Conference on Database Technologies

Nowadays privacy becomes a major concern and many research efforts have been dedicated to the development of privacy protecting technology. Anonymization techniques provide an e±cient approach to protect data privacy. We recently proposed a systematic clustering1 method based on k- anonymization technique that minimizes the information loss and at the same time assures data quality. In this paper, we extended our previous work on the systematic clustering method to l-diversity model that assumes that every group of indistinguishable records contains at least l distinct sensitive attributes values. The proposed technique adopts to group similar data together with l-diverse sensitive values and then anonymizes each group individually. The structure of systematic clustering problem for l-diversity model is defined, investigated through paradigm and is implemented in two steps, namely clustering step for k- anonymization and l-diverse step. Finally, two algorithms of the proposed problem in two steps are developed and shown that the time complexity is in O(n^2/k) in the first step, where n is the total number of records containing individuals concerning their privacy and k is the anonymity parameter for k-anonymization

DESIGNING EARTHQUAKE-RESISTANT FOUNDATIONS: A GEOTECHNICAL PERSPECTIVE ON SEISMIC LOAD DISTRIBUTION AND SOIL-STRUCTURE INTERACTION

The design of earthquake-resistant foundations is a critical aspect of geotechnical engineering, particularly in regions susceptible to seismic activity. This study explores the role of seismic load distribution and soil-structure interaction in the development of resilient foundation systems. By integrating advanced geotechnical analysis techniques, the research examines various soil types, foundation materials, and structural configurations to identify the optimal conditions for mitigating seismic impacts. Emphasis is placed on understanding the interaction between soil properties, foundation stiffness, and seismic forces, with the goal of improving the safety and durability of built environments. The findings contribute to better predictive models for designing foundations that can withstand seismic loads while ensuring long-term stability

K−means clustering microaggregation for statistical disclosure control

This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of K− means clustering problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the K− means clustering algorithm is compared against the most recent microaggregation methods. Experimental results show that K− means clustering algorithm incurs less information loss than the latest microaggregation methods for all of the test situations

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

— Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer’s disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge, a novel framework has been developed for identifying important frequency sub-bands within EEG signals for MCI detection. In the proposed scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time frames. Then, four frequency sub-bands are extracted from each segment, and spectrogram images are generated for each sub-band as well as for the full filtered frequency band signal segments. This process produces five different sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually on those image sets to perform the classification task. Finally, the obtained results for the tested four sub-bands are compared with the results obtained using the full bandwidth. Our proposed framework was tested on two MCI datasets, and the results indicate that the 16-32 Hz sub-band range has the greatest impact on MCI detection, followed by 4-8 Hz. Furthermore, our framework, utilizing the full frequency band, outperformed existing state-ofthe-art methods, indicating its potential for developing diagnostic tools for MCI detection

Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing

In cloud computing, there have led to an increase in the capability to store and record personal data ( microdata ) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k -anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature

A new estimate of carbon for Bangladesh forest ecosystems with their spatial distribution and REDD+ implications

In tropical developing countries, reducing emissions from deforestation and forest degradation (REDD+) is becoming an important mechanism for conserving forests and protecting biodiversity. A key prerequisite for any successful REDD+ project, however, is obtaining baseline estimates of carbon in forest ecosystems. Using available published data, we provide here a new and more reliable estimate of carbon in Bangladesh forest ecosystems, along with their geo-spatial distribution. Our study reveals great variability in carbon density in different forests and higher carbon stock in the mangrove ecosystems, followed by in hill forests and in inland Sal (Shorea robusta) forests in the country. Due to its coverage, degraded nature, and diverse stakeholder engagement, the hill forests of Bangladesh can be used to obtain maximum REDD+ benefits. Further research on carbon and biodiversity in under-represented forest ecosystems using a commonly accepted protocol is essential for the establishment of successful REDD+ projects and for the protection of the country’s degraded forests and for addressing declining levels of biodiversity

Did national holidays accelerate COVID-19 diffusion during the first phase of the pandemic in Bangladesh?

Bangladesh registered 20,117,32 confirmed cases of COVID-19 and the death toll crossed the grim milestone of 29,323 across the country as of August 31st, 2022. Despite the enforcement of stringent COVID-19 measures, the country witnessed an accelerated diffusion of coronavirus cases during the national events, inclusive of short festivals, in 2020. The present study aims to examine the association between these national holidays and the COVID-19 trasmission rate in Bangladesh. We employed a mathematical model and calculated the instantaneous reproduction number, Rt, of the 64 districts in Bangladesh to check the dynamics of COVID-19 diffusion. The comprehensive analysis shows a notable escalation of Rt value and thus the enhanced transmission rate in Dhaka and in all industrialized cities during the major events such as, garments reopening and religious holidays in Bangladesh. We further showcase the COVID-19 diffusion explicitly in Dhaka Division at the first phase of the pandemic in Bangladesh. Based on our analysis, a set of measures, including restricted public mobility and the celebration of festivals, alongside improving the public’s awareness of the situation, has been recommended to evade the future pandemic risks while running the national festival activities in Bangladesh