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

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

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

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

New multi-dimensional sorting based k-anonymity microaggregation for statistical disclosure control

In recent years, there has been an alarming increase of online identity theft and attacks using personally identifiable information. The goal of privacy preservation is to de-associate individuals from sensitive or microdata information. Microaggregation techniques seeks to protect microdata in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. 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 new microaggregation technique for Statistical Disclosure Control (SDC). It consists of two stages. In the first stage, the algorithm sorts all the records in the 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 an optimal microaggregation method is used to create k-anonymous clusters while minimizing the information loss. It works by taking the sorted data and simultaneously creating two distant clusters using the two extreme sorted values as seeds for the clusters. The performance of the proposed technique is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithm has the lowest information loss compared with a basket of techniques in the literature

Estimating income-related and area-based inequalities in mental health among nationally representative adolescents in Australia: the concentration index approach

Despite the awareness of the importance of mental health problems among adolescents in developed countries like Australia, inequality has not been widely researched. This study, is therefore, aimed to measure and compare household income-related and area-based socioeconomic inequalities in mental health problems (bullying victimization, mental disorders – single and multiple, self-harm and suicidality – ideation, plan and attempt) among Australian adolescents aged 12-17 years. Young Minds Matter (YMM) - the 2nd national cross-sectional mental health and well-being survey involving Australian children and adolescents conducted in 2013-14, was used in this study to select data for adolescents aged 12-17 years (n=2521). Outcome variables included: bullying, mental disorders, self-harm, and suicidal ideation, plan and attempt. The Erreygers’s corrected concentration index (CI) approach was used to measure the socioeconomic inequalities in mental health problems using two separate rank variables – equivalised household income quintiles and area-based Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) quintiles. The prevalence of mental health problems in the previous 12-months among these study participants were: bullying victimization (31.1%, 95% CI: 29%-33%), mental disorder (22.9%, 95% CI: 21%-24%), self-harm (9.1%, 95% CI: 8%-10%), suicidal ideation (8.5%, 95% CI: 7%-10%), suicidal plan (5.9%, 95% CI: 5%-7%) and suicidal attempt (2.8%, 95% CI: 2%-3%). The concentration indices (CIs) were statistically significant for bullying victimization (CI=-0.049, p=0.020), multiple mental disorders (CI=-0.088, p=<0.001), suicidal ideation (CI=-0.023, p=0.047) and suicidal attempt (CI=-0.021, p=0.002), implying pro-poor socioeconomic inequalities based on equivalized household income quintiles. Similar findings revealed when adolescents mental health inequalities calculated on the basis of area based IRSAD (Index of Relative Socio-economic Advantage and Disadvantage) quintiles. Over-all, adolescents from economically worse-off families experienced more mental health-related problems compared to those from economically better-off families. This has implications for prevention strategies and government policy in order to promote mental health and provide equitable healthcare facility

Glycemic Index Values of Rice Varieties that are Commonly Available in Markets in Bangladesh

Glycemic Index (GI) of six common rice varieties in the local markets of Bangladesh was assessed and categorized in this study to investigate manipulative varietal performance for the time being. After overnight fasting, each of ten selected healthy non-diabetic volunteers (males and females in ratio of 1:1) was fed with reference food (50 g glucose) and test foods (50 g carbohydrate-containing different rice varieties) in every two days intervals. After feeding, glucose levels (mmol/l) were measured at 0, 15, 30, 45, 60, 90 and 120 minutes. Incremental Area Under Curve (IAUC) of reference food and test food (avoiding the area beneath the baseline of reference food) was calculated to measure GI values. Amylose content (%) of different test foods was measured from the standard curve obtained from the spectrophotometric analysis after alcoholic-alkaline gelatinization that was followed by acidification and iodine mixing. The result showed that the GI values were 59.7±3.4; 50.5±2.6; 57.8±2.8; 51.3±2.3; 56.9±3.9 and 44.6±2.1, while the amylose content (%) were 23.6±0.6; 26.7±0.9; 21.3±0.7; 28.3±1.1; 22.2±2.3 and 29.8±1.5 for Nizershail, BRRI Dhan 29, Chinigura, Kalijira, Hybrid Hera Dhan 12 and Sworna, respectively. Moreover, the existing inverse relationship between the GI values and amylose content in this study was similar to other researchers’ findings. Categorization of the test foods based on the observed GI values ranked Sworna, BRRI Dhan 29 and Kalijira as low GI rice varieties that could be beneficial for consumption by diabetics as well as healthy individuals

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

BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP

Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing (NLP) for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various tasks, mostly in English, they have not yet undergone thorough evaluation in under-resourced languages such as Bengali (Bangla). In this paper, we evaluate the performance of LLMs for the low-resourced Bangla language. We select various important and diverse Bangla NLP tasks, such as abstractive summarization, question answering, paraphrasing, natural language inference, text classification, and sentiment analysis for zero-shot evaluation with ChatGPT, LLaMA-2, and Claude-2 and compare the performance with state-of-the-art fine-tuned models. Our experimental results demonstrate an inferior performance of LLMs for different Bangla NLP tasks, calling for further effort to develop better understanding of LLMs in low-resource languages like Bangla.Comment: First two authors contributed equall

Dirhenium carbonyl compounds bearing cis-1,2-bis(diphenylphosphino)ethylene and cis-1,2-bis(diphenylphosphino)ethylene oxide ligands

Reaction of [Re2(CO)9(MeCN)] with cis-1,2-bis(diphenylphosphino)ethylene (cis-Ph2PCH=CHPPh2) in boiling benzene (80 °C) afforded two compounds, ax-[Re2(CO)9(κ1-cis-Ph2PCH=CHPPh2)] (1) and ax-[Re2(CO)9{κ1-cis-Ph2PCH=CHPh2P(O)}] (2) where the ligand is axially coordinated in a κ1 monodentate fashion through phosphorus. The close-bridged compound [Re2(CO)8(μ-κ2-cis-Ph2PCH=CHPPh2)] (3) was obtained from a similar reaction of the same ligand with [Re2(CO)8(NCMe)2] in refluxing benzene. In this case the diphosphine is equatorially coordinated to two Re atoms in a symmetrical bridging fashion. Compounds 1−3 have been characterized by IR, 1H NMR, 31P{1H} NMR spectroscopy and single crystal X-ray diffraction analyses