On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases

This paper presents a possibly pioneering endeavor to tackle the microaggregation techniques (MATs) in secure statistical databases by resorting to the principles of associative neural networks (NNs). The prior art has improved the available solutions to the MAT by incorporating proximity information, and this approach is done by recursively reducing the size of the data set by excluding points that are farthest from the centroid and points that are closest to these farthest points. Thus, although the method is extremely effective, arguably, it uses only the proximity information while ignoring the mutual interaction between the records. In this paper, we argue that interrecord relationships can be quantified in terms of the following two entities: 1) their ldquoassociationrdquo and 2) their ldquointeraction.rdquo This case means that records that are not necessarily close to each other may still be ldquogrouped,rdquo because their mutual interaction, which is quantified by invoking transitive-closure-like operations on the latter entity, could be significant, as suggested by the theoretically sound principles of NNs. By repeatedly invoking the interrecord associations and interactions, the records are grouped into sizes of cardinality ldquok,rdquo where k is the security parameter in the algorithm. Our experimental results, which are done on artificial data and benchmark real-life data sets, demonstrate that the newly proposed method is superior to the state of the art not only based on the information loss (IL) perspective but also when it concerns a criterion that involves a combination of the IL and the disclosure risk (DR)

The Influence of Social Media on Arabic Writing Skills among Jordanian 10th Graders'

This study aimed to investigate the influence of using social media on the 10th graders' progress in writing skills, and their attitudes towards writing at Naour District in Amman schools, and the effect of gender, academic qualifications,  educational and professional experiences. A five-point questionnaire, and a standardized writing test were sophisticated.  It included three composition questions to get three paragraphs based on  writing. The sample of the randomized study consisted of (1064) students of the 10th grade. The study showed,  there were no significant differences in the effect of social media towards writing skills for 10th graders’ due to academic qualification on third domain, whereas there were significant differences on the effect of social media towards Arabic writing proficiency due to academic qualification in the first and second domains. There were also statistically significant differences on the effect of social media towards writing proficiency for 10th graders’. Keywords: Social Media, Writing Skills, 10th Graders, Amman schools, Teaching Methods. DOI: 10.7176/JEP/11-36-18 Publication date: December 31st 202

On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases

We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing micro-aggregation techniques (MATs), it is expedient to incorporate information about the dependence between the random variables in the micro-data file. This can be achieved by pre-processing the micro-data before invoking any MAT, in order to extract the useful dependence information from the joint probability distribution of the variables in the micro-data file, and then accomplishing the micro-aggregation on the "maximally independent" variables-thus confirming the conjecture [A conjecture, which was recently proposed by Domingo-Ferrer et al. (IEEE Trans Knowl Data Eng 14(1):189-201, 2002), was that the phenomenon of micro-aggregation can be enhanced by incorporating dependence-based information between the random variables of the micro-data file by working with (i.e., selecting) the maximally independent variables. Domingo-Ferrer et al. have proposed to select one variable from among the set of highly correlated variables inferred via the correlation matrix of the micro-data file. In this paper, we demonstrate that this process can be automated, and that it is advantageous to select the "most independent variables" by using methods distinct from those involving the correlation matrix.] of Domingo-Ferrer et al. Our results, on real life and artificial data sets, show that including such information will enhance the process of determining how many variables are to be used, and which of them should be used in the micro-aggregation process

The Effectiveness of the Conceptual Maps Strategy in Improving Conversational Skills of the Upper Basic Stage Students in Jordan

This study aimed to investigate the effect of the conceptual maps on improving the conversational skills of the students in the upper basic stage in Jordan. Data was collected via from a test developed by researcher which consisted of (50) items. The sample of the study consisted of (72) students, (9th and 10th) grades, randomly selected from one of Aljamia District schools in Amman, distributed in two groups: experimental and control, then applied the same tribal test as a post-test for the same groups after teaching the experimental group students on how to use the concept of conceptual map. The results showed that there was a positive effect among the experimental group in the field of conversational skills, and there were statistically significant differences among the students who were suffering from speech anxiety while talking with their teacher or classmates. Keywords: Conversational Skills, Traditional Education, Map concept, Upper Basic Stage Students

Attitudes of Teachers Towards Blended Learning and Their Training Needs

This study aimed to showcase the attitudes of upper basic stage teachers towards blended learning and their training needs in Jordan. The study employed a descriptive correlational methodology and formulated two instruments, confirming their validity and reliability. Data was collected from a sample of (119) educators, comprising both male and female teachers, chosen through random selection. The study’s results showed that the attitudes of teachers towards blended learning were medium, with a total arithmetic mean of (3.60), and the degree of training needs to employ blended learning in teaching was high, with a total arithmetic mean of (3.86). The study concluded that there is a positive correlation between teachers attitudes toward blended learning and their training needs. The study reached several recommendations, taking advantage of the positive attitudes of primary school teachers towards blended learning in teaching and profiting from its educational features

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis

Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing smart city infrastructures. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus of this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared as opposed to sharing data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system is evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed

The market response to the recognition of bad debt Contagion effects and competitive effects in the banking sector following problem loan write-offs

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN026698 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Video Content Search System for Better Students Engagement in the Learning Process

As a component of the e-learning educational process, content plays an essential role. Increasingly, the video-recorded lectures in e-learning systems are becoming more important to learners. In most cases, a single video-recorded lecture contains more than one topic or sub-topic. Therefore, to enable learners to find the desired topic and reduce learning time, e-learning systems need to provide a search capability for searching within the video content. This can be accomplished by enabling learners to identify the video or portion that contains a keyword they are looking for. This research aims to develop Video Content Search system to facilitate searching in educational videos and its contents. Preliminary results of an experimentation were conducted on a selected university course. All students needed a system to avoid time-wasting problem of watching long videos with no significant benefit. The statistics showed that the number of learners increased during the experiment. Future work will include studying impact of VCS system on students’ performance and satisfaction