Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process.
Kota Padang merupakan kota budaya dan terletak pada lintasan geografis yang sangat strategis. Potensi wisata Kota Padang yang begitu besar belum dapat dioptimalkan dengan baik karena minimnya media promosi yang digunakan oleh pemerintah. Pariwisata sebagai salah satu kegiatan yang komprehensif, memerlukan suatu sistem yang dapat menyajikan informasi mengenai lokasi wisata dan informasi penunjang kepariwisataan seperti infomasi peta lokasi dan rute menuju lokasi objek wisata tersebut. Aplikasi WebGis dipilih karena penyajian informasinya lebih menarik dan sangat menggambarkan kondisi sebenarnya yang ditampilkan dalam peta, baik peta garis maupun model permukaan digital. Aplikasi Sistem Informasi Geografis Pariwisata Kota Padang yang dikembangkan dengan kerangka website HTML, dengan bahasa pemograman (JavaScript dan PHP), MySql sebagai basis data dan Google Maps Api. Hasil akhir dari penelitian ini adalah terciptanya aplikasi WebGis yang dapat menyajikan informasi pariwisata kota padang yang terintegrasi, terinci dan dapat di akses dengan mudah.
In a 1961 lecture to celebrate MIT's centennial, John McCarthy proposed the vision of utility computing, including three key concepts of pay-per-use service, large computer and private computer. Six decades have passed, but Mc- Carthy's computing utility vision has not yet been fully realized, despite advances in grid computing, services computing and cloud computing. This paper presents a perspective of computing utility called Information Superbahn, building on recent advances in cloud computing. This Information Superbahn perspective retains McCarthy's vision as much as possible, while making essential modern requirements more explicit, in the new context of a networked world of billions of users, trillions of devices, and zettabytes of data. Computing utility offers pay-per-use computing services through a 1) planet-scale, 2) low-entropy and 3) high-goodput utility. The three salient characteristics of computing utility are elaborated. Initial evidence is provided to support this viewpoint.
In the era of the Internet of Things, Bluetooth low energy (BLE/BTLE) plays an important role as a well-known wireless communication technology. While the security and privacy of BLE have been analyzed and fixed several times, the threat of side-channel attacks to BLE devices is still not well understood. In this work, we highlight a side-channel threat to the re-keying protocol of BLE. This protocol uses a fixed long term key for generating session keys, and the leakage of the long term key could render the encryption of all the following (and previous) connections useless. Our attack exploits the side-channel leakage of the re-keying protocol when it is implemented on embedded devices. In particular, we present successful correlation electromagnetic analysis and deep learning based profiled analysis that recover long term keys of BLE devices. We evaluate our attack on an ARM Cortex-M4 processor (Nordic Semiconductor nRF52840) running Nimble, a popular open-source BLE stack. Our results demonstrate that the long term key can be recovered within only a small amount of electromagnetic traces. Further, we summarize the features and limitations of our attack, and suggest a range of countermeasures to prevent it.
Current Retinex-based image enhancement methods with fixed scale filters cannot adapt to situations involving various depths of field and illuminations. In this paper, a simple but effective method based on adaptive full-scale Retinex (AFSR) is proposed to clarify underwater images or videos. First, we design an adaptive full-scale filter that is guided by the optical transmission rate to estimate illumination components. Then, to reduce the computational complexity, we develop a quantitative mapping method instead of non-linear log functions for directly calculating the reflection component. The proposed method is capable of real-time processing of underwater videos using temporal coherence and Fourier transformations. Compared with eight state-of-the-art clarification methods, our method yields comparable or better results for image contrast enhancement, color-cast correction and clarity.
