Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Deep learning in agriculture: A survey

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

All one needs to know about fog computing and related edge computing paradigms: A complete survey

A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Automated Planning: Theory and Practice.

The autonomous vehicle is the evolutionary goal of designing Advanced Driver Assistance System, (ADAS) to the point where human assistance is not needed anymore. To create a broad drivable geographical area and mapping for route planning we need robust and efficient semantic segmentation algorithm. Convolutional Neural Networks (CNN) have been able to achieve state of the art performance for tasks such as Image classification, Face recognition and Detection. However semantic segmentation has remained a challenging problem in the field of computer vision. With the help of convolution neural networks, we have witnessed prolific results over time. We propose a convolutional neural network model which uses Fully Convolutional Neural Network (FCN) with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on the Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view datasets. Our method achieves a mIoU accuracy of 92.04 percent.

/pdf/deconvolutional-pixel-layer-model-for-road-segmentation-2hjv3gb59e.pdf

Deconvolutional Pixel Layer Model for Road segmentation without Human Assistance.

Optimizing Data Integration Queries over Web Data Sources (OPTIQ).

Every modern household owns at least a dozen of IoT devices like smart speakers, video doorbells, smartwatches, where most of them are equipped with a Keyword spotting (KWS) system-based digital voice assistant like Alexa. The state-of-the-art KWS systems require a large number of operations, higher computation, memory resources to show top performance. In this paper, in contrast to existing resource-demanding KWS systems, we propose a light-weight temporal convolution based KWS system named OWSNet, that can comfortably execute on a variety of IoT devices around us and can accurately spot multiple keywords in real-time without disturbing the device’s routine functionalities. When OWSNet is deployed on consumer IoT devices placed in the workplace, home, etc., in addition to spotting wake/trigger words like ‘Hey Siri’, ‘Alexa’, it can also accurately spot offensive words in real-time. If regular wake words are spotted, it activates the voice assistant; else if offensive words are spotted, it starts to capture and stream audio data to speech analytics APIs for autonomous threat and insecurities detection in the scene. The evaluation results show that the OWSNet is faster than state-of-the-art models as it produced $\approx$ 1-74 times faster inference on Raspberry Pi 4 and $\approx$ 1-12 times faster inference on NVIDIA Jetson Nano. In this paper, to optimize IoT use-case models like OWSNet, we present a generic multi-component ML model optimization sequence that can reduce the memory and computation demands of a wide range of ML models thus enabling their execution on low resource, cost, power IoT devices.

/pdf/owsnet-towards-real-time-offensive-words-spotting-network-4asjwcag1k.pdf

OWSNet: Towards Real-time Offensive Words Spotting Network for Consumer IoT Devices

Politecnico di Milano, DEIB, Milan, Italy {name.lastname}@polimi.it | yehiamohamed.abosedera@mail.polimi.it University of Zurich, Zurich, Switzerland dellaglio@ifi.uzh.ch Insight Center for Data Analytics, Galway, Ireland ali.intizar@insight-centre.org Technichal University of Berlin, Berlin, Germany danh.lephuoc@tu-berlin.de University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland jean-paul.calbimonte@hevs.ch

/pdf/vocals-describing-streams-on-the-web-2twnclqzcx.pdf

Muhammad Intizar Ali

Papers

Deconvolutional Pixel Layer Model for Road segmentation without Human Assistance.

Optimizing Data Integration Queries over Web Data Sources (OPTIQ).

OWSNet: Towards Real-time Offensive Words Spotting Network for Consumer IoT Devices

Vocals: Describing streams on the web