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.

A model capable of explaining the different factors that impact the price point is essential for the needs of the market. It can also inspire confidence in buyers and sellers about the price point offered. To achieve these objectives, we have been working with the team at MartEye, a startup based in Portershed in Galway City, Ireland. Through this paper, we report our work-in-progress research towards building a smart video analytic platform, leveraging Explainable AI techniques.

Towards Designing an Explainable-AI based Solution for Livestock Mart Industry

Smart doorbells have been playing an important role in protecting our modern homes. Existing approaches of sending video streams to a centralized server (or Cloud) for video analytics have been facing many challenges such as latency, bandwidth cost and more importantly users' privacy concerns. To address these challenges, this paper showcases the ability of an intelligent smart doorbell based on Federated Deep Learning, which can deploy and manage video analytics applications such as a smart doorbell across Edge and Cloud resources. This platform can scale, work with multiple devices, seamlessly manage online orchestration of the application components. The proposed framework is implemented using state-of-the-art technology. We implement the Federated Server using the Flask framework, containerized using Nginx and Gunicorn, which is deployed on AWS EC2 and AWS Serverless architecture.

A Demonstration of Smart Doorbell Design Using Federated Deep Learning.

Nowadays a handful applications are designed to consume dynamic real-time continuous stream data from IoT, Social network, Smart sensors and more. Several RDF Stream Processing (RSP) engines are available to query those data streams. Application designers have the freedom to select the best available RSP engine based on their application requirements. However, this selection needs to be done at design time resulting in early bound rigid solutions that are unable to adapt to changing application requirements. In this paper, we have evaluated two most popular RSP engines to proof that adaptivity is required to bridge the gap between RSP engines and applications requirement. Then we propose an adaptive middleware to adapt to dynamic application requirements during run-time. Moreover, adaptive middleware includes input and output control, monitoring the status of the underlying RSP engines, as a result the adaptive middleware is essential when single or multiple instances of same type engines are available.

On the need for applications aware adaptive middleware in real-time RDF data analysis (short paper)

The concept of the Internet of Things (IoT) is a reality now. This paradigm shift has caught everyones attention in a large class of applications, including IoT-based video analytics using smart doorbells. Due to its growing application segments, various efforts exist in scientific literature and many video-based doorbell solutions are commercially available in the market. However, contemporary offerings are bespoke, offering limited composability and reusability of a smart doorbell framework. Second, they are monolithic and proprietary, which means that the implementation details remain hidden from the users. We believe that a transparent design can greatly aid in the development of a smart doorbell, enabling its use in multiple application domains. 
To address the above-mentioned challenges, we propose a distributed framework to orchestrate video analytics across Edge and Cloud resources. We investigate trade-offs in the distribution of different software components over a bespoke/full system, where components over Edge and Cloud are treated generically. This paper evaluates the proposed framework as well as the state-of-the-art models and presents comparative analysis of them on various metrics (such as overall model accuracy, latency, memory, and CPU usage). The evaluation result demonstrates our intuition very well, showcasing that the AWS-based approach exhibits reasonably high object-detection accuracy, low memory, and CPU usage when compared to the state-of-the-art approaches, but high latency.

A Distributed Framework to Orchestrate Video Analytics Applications.

In this paper, we propose a domain agnostic and query driven approach to monitor, assess, and analyze quality of the linked data hosted by public SPARQL endpoints. We identified various quality related metrics for linked datasets and used linked data vocabulary to represent quality information. We provide a Linked Data Quality (LDQ) dataset, which is generated after conducting various quality related tests over a few public SPARQL endpoints. Our main goal in this paper is to provide a platform for monitoring, assessing and analyzing linked data quality. Data consumers can also execute various analytical queries over LDQ to analyze quality related metrics of the public SPARQL endpoints. We hope that LDQ will increase data consumer’s confidence over public SPARQL endpoints and will support the wide adoption of these datasets in various linked data applications.

/pdf/assessing-monitoring-and-analyzing-linked-data-quality-in-17b4r05zmt.pdf

Muhammad Intizar Ali

Papers

Towards Designing an Explainable-AI based Solution for Livestock Mart Industry

A Demonstration of Smart Doorbell Design Using Federated Deep Learning.

On the need for applications aware adaptive middleware in real-time RDF data analysis (short paper)

A Distributed Framework to Orchestrate Video Analytics Applications.

Assessing, Monitoring and Analyzing Linked Data Quality in Public SPARQL Endpoints.