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

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

Teknologi Smartphone Android dan Aplikasinya sebagai Pengendali Pintu Air Daerah Aliran Sungai (DAS)

Pengoperasian pintu air pada aliran sungai saat ini masih menggunakan sistem manual dengan menggunakan campur tangan tenaga manusia untuk pengendali buka tutupnya. Hal ini mendorong peneliti untuk melakukan penelitian dengan sistem kendali berbasis smartphone berbasis android. Dalam penelitian ini penulis menggunakan smartphone android sebagai sarana untuk kendali pintu air. Smartphone dipilih oleh penulis karena saat ini menjadi alat komunikasi sehari-hari yang mobile di semua kalangan masyarakat sedangkan andriod merupakan sistem operasi yang sebagian besar dipakai pada smartphone tersebut. Smartphone berbasis android akan secara langsung mengendalikan buka tutup pintu Daerah Aliran Sungai (DAS) dengan menggunakan program aplikasi. Masukan dari aplikasi akan memberikan informasi perintah yang diberikan, selanjutnya akan diterima oleh sistem kendali yang terhubung pada mekanisme gerakan mekanik pintu air sungai. Peralatan kendali menggunakan perangkat Arduino Uno yang mampu mengubah signal digital menjadi gerakan mekanik dalam mengoperasikan pintu DAS. Hasil penelitian yang diperoleh, penggunaan perangkat kendali Arduino Uno tidak menyediakan ruang dalam pengelolaan database secara online, maka dibutuhkan software pendukung lainnya untuk mengatasi hal tersebut. Sebagai pengendali dan sistem android merupakan media penghubung informasi saling terkait merupakan sumber informasi yang terbuka