A positive stigma for child labor ?

The authors introduce a simple empirical model that assumes a positive stigma (or norm) toward child labor that is common in some developing countries. They illustrate the positive stigma model using data from Guatemala. Controlling for several child and household-level characteristics, the analysis uses two instruments for measuring stigma: a child's indigenous background and the household head's childhood work experience.Street Children,Youth and Governance,Children and Youth,Labor Policies,Primary Education

ENVIRONMENTAL EFFECTS ON BIRTH WEIGHT IN BEETAL GOAT KIDS

Data on pedigree, breeding and performance records (N=1850) of Beetal goats maintained at the Angora Goat Farm Rakh Kharewala, District Layyah, Livestock Production Research Institute, Bahadurnagar District. Okara and Livestock Experiment Station, Allahdad (Jahanian) District Khenawal during the period from 1988 to 2000 were used. Least squares analysis revealed that year of birth, sire, flock, sex of kid and type of birth were significant (P<0.01) sources of variation for birth weight in Beetal kids. The kids born at Bahadurnagar were heavier (3.65 ± 0.13 kg) as compared to the kids born at Allahdad (3.55 ± 0.08 kg) or Rakh Kharewala (2.96 ± 0.05 kg). Birth weights for male and female kids were 3.48 ± 0.06 and 3.29 ± 0.06 kg, respectively. Single born kids were heavier (3.69 ± 0.06 kg) than twins (3.37 ± 0.06 kg) and triplets (3.08 ± 0.08 kg). There was an appreciable twining rate (47.9%) in these flocks

Using SERVQUAL to determine Generation Y’s satisfaction towards hoteling industry in Malaysia

Purpose: The purpose of this paper is to use SERVQUAL to measure Generation Y’s (Gen Y) perceived service quality and its effects on their satisfaction toward the Malaysian hotel industry. Design/methodology/approach: The required data were collected through questionnaire, distributed to 200 respondents in four areas of Klang Valley. The collected data were put through multiple regression to identify the effect of SERVQUAL dimensions on service quality. Findings: The results reveal that all the elements of SERVQUAL, except tangibility, had a significant and positive relationship with customer satisfaction. Research limitations/implications: It is a niche area research which is done on a small population in a specified geographical area within Malaysia, though its research implications are significant and add significantly to the tourism literature with respect to Gen Y. Practical implications: This research holds importance in the growing service tourism and hoteling industry in Malaysia, where Gen Y holds a key economic position and is predicted to grow even further in the near future. Originality/value: It is a niche area research done on very specific consumers in Malaysia. It, therefore, adds to the emerging field of tourism in relation to Gen Y

Measuring, Characterizing, and Detecting Facebook Like Farms

Social networks offer convenient ways to seamlessly reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes, aka like farms, has emerged alongside Facebook's official targeted advertising platform. Nonetheless, there is little work that systematically analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics, and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms which spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a range of features, grouped into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves precision higher than 99% and 93% recall.Comment: To appear in ACM Transactions on Privacy and Security (TOPS

Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective

Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this paper, first, we summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine learning algorithms

Characterizing Key Stakeholders in an Online Black-Hat Marketplace

Over the past few years, many black-hat marketplaces have emerged that facilitate access to reputation manipulation services such as fake Facebook likes, fraudulent search engine optimization (SEO), or bogus Amazon reviews. In order to deploy effective technical and legal countermeasures, it is important to understand how these black-hat marketplaces operate, shedding light on the services they offer, who is selling, who is buying, what are they buying, who is more successful, why are they successful, etc. Toward this goal, in this paper, we present a detailed micro-economic analysis of a popular online black-hat marketplace, namely, SEOClerks.com. As the site provides non-anonymized transaction information, we set to analyze selling and buying behavior of individual users, propose a strategy to identify key users, and study their tactics as compared to other (non-key) users. We find that key users: (1) are mostly located in Asian countries, (2) are focused more on selling black-hat SEO services, (3) tend to list more lower priced services, and (4) sometimes buy services from other sellers and then sell at higher prices. Finally, we discuss the implications of our analysis with respect to devising effective economic and legal intervention strategies against marketplace operators and key users.Comment: 12th IEEE/APWG Symposium on Electronic Crime Research (eCrime 2017