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.

Due to the decentralised and autonomous architecture of the Web of Data, data replication and local deployment of SPARQL endpoints is inevitable. Nowadays, it is common to have multiple copies of the same dataset accessible by various SPARQL endpoints, thus leading to the problem of selecting optimal data source for a user query based on data properties and requirements of the user or the application. Quality of Service (QoS) parameters can play a pivotal role for the selection of optimal data sources according to the user’s requirements. QoS parameters have been widely studied in the context of web service selection. However, to the best of our knowledge, the potential of associating QoS parameters to SPARQL endpoints for optimal data source selection has not been investigated.

How Good Is Your SPARQL Endpoint

With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be flashed and executed on tiny IoT devices, particularly the embedded systems. Edge analytics eliminates expensive device-to-cloud communications, thereby producing intelligent devices that can perform energy-efficient real-time offline analytics. Any increase in the training data results in a linear increase in the size and space complexity of the trained ML models, making them unable to be deployed on IoT devices with limited memory. To alleviate the memory issue, a few studies have focused on optimizing and fine-tuning existing ML algorithms to reduce their complexity and size. However, such optimization is usually dependent on the nature of IoT data being trained. In this paper, we presented an approach that protects model quality without requiring any alteration to the existing ML algorithms. We propose SRAM-optimized implementation and efficient deployment of widely used standard/stable ML-frameworks classifier versions (e.g., from Python scikit-learn). Our initial evaluation results have demonstrated that ours is the most resource-friendly approach, having a very limited memory footprint while executing large and complex ML models on MCU-based IoT devices, and can perform ultra-fast classifications while consuming 0 bytes of SRAM. When we tested our approach by executing it on a variety of MCU-based devices, the majority of models ported and executed produced 1-4x times faster inference results in comparison with the models ported by the sklearn-porter, m2cgen, and emlearn libraries.

/pdf/ultra-fast-machine-learning-classifier-execution-on-iot-3cf5be7ah4.pdf

Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption

This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering level. We propose OTA-TinyML to enable resource-constrained IoT devices to perform end-to-end fetching, storage, and execution of many TinyML models. OTA-TinyML loads the C source file of ML models from a web server into the embedded IoT devices via HTTPS. OTA-TinyML is tested by performing remote fetching of six types of ML models, storing them on four types of memory units, then loading and executing on seven popular MCU boards.

OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices

Training a problem-solving Machine Learning (ML) model using large datasets is computationally expensive and requires a scalable distributed training platform to complete training within a reasonable time frame. In this paper, we propose a novel concept where, instead of distributed training within a GPU cluster, we train one ML model by utilizing the idle hardware of numerous resource-constrained IoT devices existing across the globe. In such a global setting, staleness and real-world network uncertainties like congestion, latency, bandwidth issues are proven to impact the model convergence speed and training scalability. To implement the novel concept, while simultaneously addressing the real-world global distributed training challenges, we present Globe2Train (G2T), a framework with two components named G2T-Cloud (G2T-C) and G2T-Device (G2T-D) that can efficiently connect together multiple IoT devices and collectively train to produce the target ML models at very high speeds. The evaluation results with analysis show how the framework components jointly eliminate staleness and improve training scalability and speed by tolerating the real-world network uncertainties and by reducing the communication-to-computation ratio.

Globe2Train: A Framework for Distributed ML Model Training using IoT Devices Across the Globe

XSPARQL is a transformation and querying language that provides an integrated access over heterogeneous data sources on the fly. It is an extension of XQuery which supports a subset of SPARQL and SQL to provide unified access over XML, RDF and RDB formats. In practical applications, data integration does not only require the integrated access over distributed heterogeneous data sources, but also the update of underlying data.

Muhammad Intizar Ali

Papers

How Good Is Your SPARQL Endpoint

Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption

OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices

Globe2Train: A Framework for Distributed ML Model Training using IoT Devices Across the Globe

Update Semantics for Interoperability among XML, RDF and RDB A Case Study of Semantic Presence in CISCO's Unified Presence Systems