Interent Usage among Female Undergraduates in Ferdowsi Univrsity, Iran

The importance of the public use of Internet has noticeably increased in people’s daily life. Internet, regarding its potential possibilities has attracted most Internet users’ prominent attention specially, students. The significant portions of students who use the Internet are females. They involve with the Internet based on their needs for accessing information, satisfying their leisure activities, transferring their information, making communication with others and so forth. This study was designed by applying the Use and Gratification theory framework to understand the Internet usage among female undergraduate students. The objectives of this study are to identify the relationship between the pattern of Internet usage, attitude towards Internet, English language knowledge, field of studies, Internet skills, problems and purpose of using Internet with the gratification of Internet usage. The present study used a survey design to achieve the objectives of the study. Non-probability sampling was employed in this study. The purposive sampling method was chosen for this study because the subjects were selected based on the specific demographical characteristics such as gender, age, education level, not working and using the Internet. A total of 319 respondents participated in the study in which 62 are from the field of English language and 257 from the other fields of humanities. Five categories of gratification for using the Internet were identified, namely, Escape, Affective, Cognitive, Social Integration, and Personal Integration. Most of the female undergraduate students used Internet for searching and getting knowledge. Finding relevant information for research was the most important purpose for students. They mostly search in Persian Google.com. The most common problem of using the Internet is that it takes too long time to download the Internet pages. The most common gratification of using the Internet was related to information gathering and learning new things. This study found no significant relationship between numbers of years in using the Internet with gratification of Internet usage. However, the relationships between attitude, purpose and frequency of Internet usage with gratifications of Internet usage are significant and positive

Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset

In this work, we compare two simple methods of tagging scientific publications with labels reflecting their content. As a first source of labels Wikipedia is employed, second label set is constructed from the noun phrases occurring in the analyzed corpus. We examine the statistical properties and the effectiveness of both approaches on the dataset consisting of abstracts from 0.7 million of scientific documents deposited in the ArXiv preprint collection. We believe that obtained tags can be later on applied as useful document features in various machine learning tasks (document similarity, clustering, topic modelling, etc.)

The relationship between personal factors and its effect on internet usage

The use of communication technology is growing rapidly in colleges and universities. The objectives of this study are to identify the effect between age, level of the study, field of the study, gender and income on attitudes toward the Internet, problems in using the Internet and gratification of Internet usage. The present study used the quantitative method by means of a questionnaire survey as a means of achieving its objectives. Sample size for this study is 440 and University Putra Malaysia is chosen as a location. Also quota sampling applied for this investigation. Personal factors in this study include (age, gender, income, level of study and field of study) of respondents. The findings of the study revealed significant relationships between age and attitudes towards Internet, problems in using the Internet and gratification of Internet usage. In addition, there is a significant relationship between educational achievement level and problems in using the Internet. There is a significant relationship between income and attitudes towards Internet, problems in using the Internet and gratification Internet usage

Experiences of using an interactive audience response system in lectures

BACKGROUND: Lectures are good for presenting information and providing explanations, but because they lack active participation they have been neglected. METHODS: Students' experiences were evaluated after exposing them to the use of voting during lectures in their paediatrics course. Questions were delivered to the students taking paediatrics course. Thirty-six students out of the total of 40 (90%) attended the opening lecture, at which the first survey concerning previous experiences of lectures was performed. Thirty-nine students (98%) answered the second series of questions at the end of the paediatrics course. RESULTS: Most of the students felt that voting improved their activity during lectures, enhanced their learning, and that it was easier to make questions during lectures than earlier. CONCLUSIONS: The students gained new, exciting insights much more often during the paediatrics course than before. We as teachers found that voting during lectures could easily overcome some of the obstacles of good lecturing

Investigating the Correlation between Performance Scores and Energy Consumption of Mobile Web Apps

Context. Developers have access to tools like Google Lighthouse to assess the performance of web apps and to guide the adoption of development best practices. However, when it comes to energy consumption of mobile web apps, these tools seem to be lacking. Goal. This study investigates on the correlation between the performance scores produced by Lighthouse and the energy consumption of mobile web apps. Method. We design and conduct an empirical experiment where 21 real mobile web apps are (i) analyzed via the Lighthouse performance analysis tool and (ii) measured on an Android device running a software-based energy profiler. Then, we statistically assess how energy consumption correlates with the obtained performance scores and carry out an effect size estimation. Results. We discover a statistically significant negative correlation between performance scores and the energy consumption of mobile web apps (with medium to large effect sizes), implying that an increase of the performance score tend to lead to a decrease of energy consumption. Conclusions. We recommend developers to strive to improve the performance level of their mobile web apps, as this can also have a positive impact on their energy consumption on Android devices

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

Learning categories with spiking nets and spike timing dependent plasticity

An exploratory study of learning a neural network for categorisation shows that commonly used leaky integrate and fire neurons and Hebbian learning can be effective. The system learns with a standard spike timing dependent plasticity Hebbian learning rule. A two layer feed forward topology is used with a presentation mechanism of inputs followed by outputs a simulated ms. later to learn Iris flower and Breast Cancer Tumour Malignancy categorisers. An exploration of parameters indicates how this may be applied to other tasks