Impact of E-Learning and Digitalization in Primary and Secondary Schools

This study examines into the impact of e-learning and digitalization in primary and secondary schools, using Greensprings School in Lagos State, Nigeria as a case study. Questionnaire was used as a data collection instrument, and descriptive statistical method was adopted for analysis. Responses from students and teachers reveal that application of e-learning technology in schools will help to promote an efficient, effective and productive way of teaching. More so, e-learning promotes better communication and helps teachers and students to share accountability for learning and achievements. The study shows that most students agreed that e-learning help students to have access to unlimited source of information; reveals connection between subjects; promotes critical thinking; and encourages students’ way of learning. The study further shows that majority of the teachers agreed that e-learning is easier and effective; helps to further develop teachers’ computer skills; and brings out the best in students. Interestingly, the two parties agreed that e-learning helps teachers and students to share accountability for learning and achievements. Keywords: E-Learning, Digitalization, Virtual Learning Environment (VLE), Greensprings, School, Lagos State, Nigeria

APLIKASI PERHITUNGAN HARGA POKOK PRODUKSI DAN HARGA POKOK PENJUALAN (STUDI KASUS : BUTIK MALLA RAMDANI, BANDUNG)

Butik Malla Ramdani merupakan perusahaan manufaktur yang memproduksi baju. Dalam proses produksi melibatkan Biaya Bahan Baku (BBB), Biaya Tenaga Kerja Langsung (BTKL), dan Biaya Overhead Pabrik (BOP). Perusahaan tidak memiliki pecatatan atas biaya-biaya yang terjadi. Dengan perkembangan teknologi informasi, maka dibangun sebuah aplikasi yang dapat membantu proses pencatatan biayabiaya yang dikeluarkan. Selain itu, aplikasi yang dibangun juga dapat membantu dalam perhitungan harga pokok produksi dan harga pokok penjualan. Aplikasi berbasis web untuk perhitungan harga pokok produksi dan harga pokok penjualan dengan metode Job Order Costing. Aplikasi ini untuk mengurangi kesalahan dalam menghitung harga pokok produksi dan harga pokok penjualan. Aplikasi ini menghasilkan jurnal umum, laporan laba-rugi,buku besar, dan grafik pertumbuhan pendapatan Aplikasi ini menggunakan framework codeigniter version 3.1.9 yang menggunakan bahasa pemrograman hypertext preprocessor (PHP) dan untuk basis data menggunakan Structured Query Language (SQL). Kata kunci : Job Order Costing, harga pokok produksi, harga pokok penjualan, CodeIgniter, SQ

Disentanglement of Correlated Factors via Hausdorff Factorized Support

A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning a models representations with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, however, rely on an often unrealistic assumption: that factors are statistically independent. In reality, factors (like object color and shape) are correlated. To address this limitation, we propose a relaxed disentanglement criterion - the Hausdorff Factorized Support (HFS) criterion - that encourages a factorized support, rather than a factorial distribution, by minimizing a Hausdorff distance. This allows for arbitrary distributions of the factors over their support, including correlations between them. We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over +60% in relative improvement over existing disentanglement methods. In addition, we find that leveraging HFS for representation learning can even facilitate transfer to downstream tasks such as classification under distribution shifts. We hope our original approach and positive empirical results inspire further progress on the open problem of robust generalization

PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning

Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation. Despite such promise, the use of synthetic image data is still limited -- and often played down -- mainly due to their lack of realism. Most works therefore rely on datasets of real images, which have often been scraped from public images on the internet, and may have issues with regards to privacy, bias, and copyright, while offering little control over how objects precisely appear. In this work, we present a path to democratize the use of photorealistic synthetic data: we develop a new generation of interactive environments for representation learning research, that offer both controllability and realism. We use the Unreal Engine, a powerful game engine well known in the entertainment industry, to produce PUG (Photorealistic Unreal Graphics) environments and datasets for representation learning. In this paper, we demonstrate the potential of PUG to enable more rigorous evaluations of vision models

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations

Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, with downstream tasks generally unknown at training time, it is difficult to deduce a priori which attributes of the data are indeed "style" and can be safely discarded. To address this, we introduce a more principled approach that seeks to disentangle style features rather than discard them. The key idea is to add multiple style embedding spaces where: (i) each is invariant to all-but-one augmentation; and (ii) joint entropy is maximized. We formalize our structured data-augmentation procedure from a causal latent-variable-model perspective, and prove identifiability of both content and (multiple blocks of) style variables. We empirically demonstrate the benefits of our approach on synthetic datasets and then present promising but limited results on ImageNet

PFOS induces behavioral alterations, including spontaneous hyperactivity that is corrected by dexamfetamine in zebrafish larvae

Perfluorooctane sulfonate (PFOS) is a widely spread environmental contaminant. It accumulates in the brain and has potential neurotoxic effects. The exposure to PFOS has been associated with higher impulsivity and increased ADHD prevalence. We investigated the effects of developmental exposure to PFOS in zebrafish larvae, focusing on the modulation of activity by the dopaminergic system. We exposed zebrafish embryos to 0.1 or 1 mg/L PFOS (0.186 or 1.858 µM, respectively) and assessed swimming activity at 6 dpf. We analyzed the structure of spontaneous activity, the hyperactivity and the habituation during a brief dark period (visual motor response), and the vibrational startle response. The findings in zebrafish larvae were compared with historical data from 3 months old male mice exposed to 0.3 or 3 mg/kg/day PFOS throughout gestation. Finally, we investigated the effects of dexamfetamine on the alterations in spontaneous activity and startle response in zebrafish larvae. We found that zebrafish larvae exposed to 0.1 mg/L PFOS habituate faster than controls during a dark pulse, while the larvae exposed to 1 mg/L PFOS display a disorganized pattern of spontaneous activity and persistent hyperactivity. Similarly, mice exposed to 0.3 mg/kg/day PFOS habituated faster than controls to a new environment, while mice exposed to 3 mg/kg/day PFOS displayed more intense and disorganized spontaneous activity. Dexamfetamine partly corrected the hyperactive phenotype in zebrafish larvae. In conclusion, developmental exposure to PFOS in zebrafish induces spontaneous hyperactivity mediated by a dopaminergic deficit, which can be partially reversed by dexamfetamine in zebrafish larvae