Exploring machine learning techniques for fake profile detection in online social networks

The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results

Development of Standardized Scientific Temper Tool

The prime aim of the present paper was to develop a standardized tool to measure scientific temper. For this purpose, the investigators organized three workshops with experts in the field of science and social sciences and the tool is developed in three phases. In the first phase, the term scientific temper had been operationally defined and four dimensions, i.e. spirit of enquiry, rational thinking, cause and effect relationship and scientific information were finalized to measure the scientific temper among people. This scientific temper tool was developed in the form of a questionnaire. The items related to above four dimensions were collected and shuffled well to prepare initial draft of the schedule. Thus the initial draft contained 70 items in it. The questionnaire was prepared in three parts, designated as FORM A, FORM B and FORM C. In the FORM A responses were recorded on Likert-scale while in FORM B questions were kept open-ended and the responses in FORM C, which constituted the core of Scientific Temper Questionnaire (STQ), were categorized into three options, from scientific to superstitious. It should be noted that all the three parts contianed identical indicators.In the second phase, the initial draft of STQ was administered on a sample of 120 students in order to determine the discriminative value and popularity value of each item for the purpose of item analysis. The reliability was established through Kuder Richardson formula and content and concurrent validity were also established. In order to develo

A Study of Environmental Awareness among Muslim Girls Pursuing Graduation Course in Bareilly Region

The present study is focused on the assessment on environmental awareness among Muslim girls pursuing graduation course in Bareilly Region. The investigators used normative survey method for the study. The sample consists of 500 Muslim girls from five degree colleges in Bareilly Region. Simple random sampling technique has been used for the selection of sample. Environmental Awareness Test was used to assess environmental awareness among Muslim girls. The major finding of study reveals that rural Muslim girls are less aware of their environment as compared to urban Muslim girls. Similarly, girls belonging to arts stream have less environmental awareness as compared to their counterparts belonging to science stream. Further, it is also found that Muslim girls, whose parents are graduate, have high level of environmental awareness that that of those girls whose parents are non –graduate

A New Species and a New Record of the Ant Genus Stigmatomma Roger (Hymenoptera: Formicidae) from India

A new species of the ant genus Stigmatomma Roger, 1859 collected from North-eastern Himalaya is described: Stigmatomma xui sp. n. Another species Stigmatomma awa (Xu & Chu, 2012) is also reported for the first time from India and is formally transferred from Ambylopone to Stigmatomma. A key is provided to distinguish the related species

Inferring physical laws by artificial intelligence based causal models

The advances in Artificial Intelligence (AI) and Machine Learning (ML) have opened up many avenues for scientific research, and are adding new dimensions to the process of knowledge creation. However, even the most powerful and versatile of ML applications till date are primarily in the domain of analysis of associations and boil down to complex data fitting. Judea Pearl has pointed out that Artificial General Intelligence must involve interventions involving the acts of doing and imagining. Any machine assisted scientific discovery thus must include casual analysis and interventions. In this context, we propose a causal learning model of physical principles, which not only recognizes correlations but also brings out casual relationships. We use the principles of causal inference and interventions to study the cause-and-effect relationships in the context of some well-known physical phenomena. We show that this technique can not only figure out associations among data, but is also able to correctly ascertain the cause-and-effect relations amongst the variables, thereby strengthening (or weakening) our confidence in the proposed model of the underlying physical process.Comment: Latex 12 pages, 16 figure

Synthesis and study of electrical properties of polyaniline and polyester nanocomposite

In this study, pure polyaniline (PANI) and PANI/polyester nanocomposites (wt % 10, 20 and 30) of thickness 10 mm have been synthesized by co-precipitation method to investigate their electrical properties. Pure PANI and its composites are then characterized using SEM, FTIR, DSC, I-V Hall and dielectric techniques. From SEM, it is observed that the particles are spherical and lying in range of micron order. To find out the nature of chemical bonds, FTIR analysis and thermal characterization (DSC) are performed. From the I-V curve, the calculated values of conductance of prepared pure PANI and PANI/PET (wt % 10, 20 and 30 nanocomposite) are obtained as 3.78×10-7, 9.7933×10-7, 9.584×10-8 and 9.882×10-9 ohm-1 respectively. From Hall measurement, electrical conductivity of pure PANI and PANI/PET (wt % 10, 20 and 30) nanocomposite is found 49.26 ×10-6, 13.82 ×10-6, 40.6 ×10-7 and 18.761 ×10-7 Ω-1cm-1 respectively. Dielectric study of PANI/PET reveals downturn in the dielectric constant value with increased frequency. The electrical resistivity of the PANI/PET nanocomposite shows high anti-static efficiency