A Smart Wizard System Suitable for Use With Internet Mobile Devices to Adjust Personal Information Privacy Settings

The privacy of personal information is an important issue affecting the confidence of internet users. The widespread adoption of online social networks and access to these platforms using mobile devices has encouraged developers to make the systems and interfaces acceptable to users who seek privacy. The aim of this study is to test a wizard that allows users to control the sharing of personal information with others. We also assess the concerns of users in terms of such sharing such as whether to hide personal data in current online social network accounts. Survey results showed the wizard worked very well and that females concealed more personal information than did males. In addition, most users who were concerned about misuse of personal information hid those items. The results can be used to upgrade current privacy systems or to design new systems that work on mobile internet devices. The system can also be used to save time when setting personal privacy settings and makes users more aware of items that will be shared with others.Comment: 16 pages, 8 figures, 2 table

PERSONAL INFORMATION PRIVACY SETTINGS OF ONLINE SOCIAL NETWORKS AND THEIR SUITABILITY FOR MOBILE INTERNET DEVICES

ABSTRAC

A Framework to Enhance Privacy-Awareness in Mobile Web Systems

In the last decade, the use of online social network sites has dramatically increased and these sites have succeeded in attracting a large number of users. The social network site has become a daily tool people use to find out about the latest news and to share details of their personal information. Many people use Internet mobile devices to browse these sites. The widespread use of some technologies unnecessarily puts the privacy of users at risk, even when these users remain anonymous. This study examines the risks to privacy surrounding the misuse of users' personal information, such as maintaining trustworthy sites, as well as privacy issues associated with sharing personal information with others. This study also develops a framework to enhance privacy awareness in mobile Web systems. A privacy framework is proposed that incorporates suitability in the design and flexibility in the use to suit different types of Web mobile devices, and provides simple ways of adjusting and creating different privacy policies. This framework allows the user to create different levels of privacy settings and to better manage the exchange of personal information with other sites. The proposed conceptual model for this study is derived from a review of the literature and the current privacy models. It shows how online users are able to create different privacy policies and set different policies to access the data. It also explains how the centrality of personal information details in one server will limit the distribution of personal information over the Internet and will provide users with more authority to control the sharing of their information with other websites. The design of the proposed framework is derived from developing other privacy models and adding new ideas that enhance the security level of protecting the privacy of users' information. The study consists of five main tasks that include two different qualitative methodologies, programming two applications and testing the framework

Alternative cancer therapy through modeling pteridines photosensitizer quantum yield singlet oxygen production using swarm-based support vector regression and extreme learning machine

AbstractPhotodynamic cancer therapy circumvents the major side effects associated with the conventional cancer treatment methods, such as chemotherapy, surgery and exposure to radiation. Experimental measurement of photosensitizer quantum yield (PQY) singlet production of oxygen through either sensitive laser spectroscopy or luminescence detection at the wavelength of 1270 nm is costly; time consuming and intensive while unreliability of chemical traps experimental approach is of serious concern. Quantitative structure–activity relationship (QSAR) computational method proposed in the literature for computing PQY of singlet oxygen production has characteristics deviation from the measured values. PQY singlet oxygen production of twenty-nine pteridines photosensitizer compounds is modeled and predicted in this present contribution using extreme learning machine (ELM) and support vector regression (SVR) with hybridized particle swarm optimization (PSO) method for ensuring combinatory parameter selection. The performances of the developed SVR-PSO computational method are assessed using mean absolute error (MAE), correlation coefficient (CC), root mean square error (RMSE) and mean absolute percentage deviation (MAPD). The developed SVR-PSO model outperforms QSAR (2016) model with performance superiority of 34.78%, 3.65%, 17.64% and 42.16% on the basis of RMSE, CC, MAE and MAPD performance measuring parameters, respectively. The developed ELM-SINE (with sine activation function) and ELM-SIG (with sigmoid activation function) respectively outperform the existing QSAR (2016) model with improvement of 6.54% and 4.70% using R-squared metric. The demonstrated outstanding performance of the present predictive models is immensely meritorious in strengthening the potentials of alternative cancer therapy and circumventing the experimental challenges of PQY singlet oxygen production determination

Personal Information Privacy Settings of Online Social Networks and Their Suitability for Mobile Internet Devices

Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users' privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. Survey results show high percentage of the use of smart phones for web services but the current privacy settings for online social networks need to be improved to support different type of mobile phones screens. Because most users use their mobile phones for Internet services, privacy settings that are compatible with mobile phones need to be developed. The method of selecting privacy settings should also be simplified to provide users with a clear picture of the data that will be shared with others. Results of this study can be used to develop a new privacy system which will help users control their personal information easily from different devices, including mobile Internet devices and computers

Sustainable Education Quality Improvement Using Academic Accreditation: Findings from a University in Saudi Arabia

Accreditation is widely considered to be a vital tool for quality assurance in higher education; however, there is disagreement in the academic community on the intended benefits of accreditation. Preparing for accreditation requires extensive financial and human resources to complete the required documentation. All accreditation agencies require improvements in institutional infrastructure, enhanced student support, appropriate learning environments, and faculty development, which can directly improve students’ learning experiences. In this paper, we explore the impact of accreditation on students’ learning by using a case study-based approach. We selected four degree programs from a University in Saudi Arabia and compared the performances of students in different courses before and after acquiring local program accreditation (NCAAA). The results highlight that although there is no direct relationship between increased student performance and acquiring accreditation, there is a significant impact on the performance of student learning. However, there is a need for sustained efforts to continuously adopt accreditation-aligned practices to gain a sustained advantage. We have presented a model that can enable academic institutions to continuously adhere to best practices even if no accreditation visit has been scheduled in the near future. This way, academic programs can consistently improve their processes and enhance student learning

Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression

Magnetic refrigeration (MR) technology stands a good chance of replacing the conventional gas compression system (CGCS) of refrigeration due to its unique features such as high efficiency, low cost as well as being environmental friendly. Its operation involves the use of magnetocaloric effect (MCE) of a magnetic material caused by application of magnetic field. Manganite-based material demonstrates maximum MCE at its magnetic ordering temperature known as Curie temperature (TC). Consequently, manganite-based material with TC around room temperature is essentially desired for effective utilization of this technology. The TC of manganite-based materials can be adequately altered to a desired value through doping with appropriate foreign materials. In order to determine a manganite with TC around room temperature and to circumvent experimental challenges therein, this work proposes a model that can effectively estimates the TC of manganite-based material doped with different materials with the aid of support vector regression (SVR) hybridized with gravitational search algorithm (GSA). Implementation of GSA algorithm ensures optimum selection of SVR hyper-parameters for improved performance of the developed model using lattice distortions as the descriptors. The result of the developed model is promising and agrees excellently with the experimental results. The outstanding estimates of the proposed model suggest its potential in promoting room temperature magnetic refrigeration through quick estimation of the effect of dopants on TC so as to obtain manganite that works well around the room temperature

Modeling energy band gap of doped TiO2 semiconductor using homogeneously hybridized support vector regression with gravitational search algorithm hyper-parameter optimization

Titanium dioxide (TiO2) semiconductor is characterized with a wide band gap and attracts a significant attention for several applications that include solar cell carrier transportation and photo-catalysis. The tunable band gap of this semiconductor coupled with low cost, chemical stability and non-toxicity make it indispensable for these applications. Structural distortion always accompany TiO2 band gap tuning through doping and this present work utilizes the resulting structural lattice distortion to estimate band gap of doped TiO2 using support vector regression (SVR) coupled with novel gravitational search algorithm (GSA) for hyper-parameters optimization. In order to fully capture the non-linear relationship between lattice distortion and band gap, two SVR models were homogeneously hybridized and were subsequently optimized using GSA. GSA-HSVR (hybridized SVR) performs better than GSA-SVR model with performance improvement of 57.2% on the basis of root means square error reduction of the testing dataset. Effect of Co doping and Nitrogen-Iodine co-doping on band gap of TiO2 semiconductor was modeled and simulated. The obtained band gap estimates show excellent agreement with the values reported from the experiment. By implementing the models, band gap of doped TiO2 can be estimated with high level of precision and absorption ability of the semiconductor can be extended to visible region of the spectrum for improved properties and efficiency