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.

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The high expressiveness of non-monotonic reasoning enables complex decision making by managing defaults, commonsense, preferences, recursion, and non-determinism, but it is computationally intensive. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art non-monotonic reasoners to be applied to streaming information in a scalable way. In this paper, we address the issue of scalability for nonmonotonic stream reasoning based on Answer Set Programming (ASP) - an expressive reasoning approach based on disjunctive logic Datalog with negation under the stable model semantics, by analyzing input dependency. We introduce an input dependency graph to represent the relationships between input events based on the structure of a given logical rule set. The input dependency graph allows us to dynamically configure the streaming window size in order to maximise the scalability of the non-monotonic reasoner. We conduct an experimental evaluation to demonstrate the effectiveness and ability of our proposed approach in improving the scalability of disjunctive logic programming with ASP in dynamic environments.

/pdf/towards-scalable-non-monotonic-stream-reasoning-via-input-2wen04p6ol.pdf

Towards Scalable Non-Monotonic Stream Reasoning via Input Dependency Analysis

The proliferation of sensor devices and services along with the advances in event processing brings many new opportunities as well as challenges. It is now possible to provide, analyze and react upon real-time, complex events in urban environments. When existing event services do not provide such complex events directly, an event service composition maybe required. However, it is difficult to determine which event service candidates or service compositions best suit users' and applications' quality-of-service requirements. A sub-optimal service composition may lead to inaccurate event detection, lack of system robustness etc. In this paper, the authors address these issues by first providing a quality-of-service aggregation schema for complex event service compositions and then developing a genetic algorithm to efficiently create near-optimal event service compositions. The authors evaluate their approach with both real sensor data collected via Internet-of-Things services as well as synthesised datasets.

QoS-Aware Stream Federation and Optimization Based on Service Composition

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams.
The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in
order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing
over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of
a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an
algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on
this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels
of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting
the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and
combining the results.

/pdf/enhancing-the-scalability-of-expressive-stream-reasoning-via-2ia83wnboo.pdf

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications However, any increase in the training data results in a linear increase in the space complexity of the trained Machine Learning (ML) models, which means they cannot be deployed on IoT devices that have limited memory To alleviate such memory issues, we recently proposed an SRAM-optimized classifier porting, stitching, and efficient deployment method in [3] This is currently the most resource-friendly approach that enables large classifiers to be comfortably executed on microcontroller unit (MCU) based IoT devices, and perform ultra-fast classifications (1--4x times faster than state-of-the-art libraries) while consuming 0 bytes of SRAM In this demo, realizing our recent SRAM-optimized approach, we port and execute 7 dataset-trained classifiers on 7 popular MCUs, and report their inference performance It is apparent from the demo results that realizing our approach makes even the slowest Atmega328P MCU perform faster unit inference than a NVIDIA Jetson Nano GPU and Raspberry Pi 4 CPU

/pdf/sram-optimized-porting-and-execution-of-machine-learning-44pvtitus0.pdf

SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: demo abstract

In today's IoT smart environments, dozens of MCU-based connected device types exist such as HVAC controllers, smart meters, smoke detectors, etc. The security conditions for these essential IoT devices remain unsatisfactory since: (i) many of them are built with cost as the driving design tenet, resulting in poor configurations and open design; (ii) their memory and computational resource constraints make it highly challenging to implement practical attack protection mechanisms; and (iii) currently, manufacturers use simplified light protocol versions to save memory for extra features (to boost sales). When such issues and vulnerabilities are exploited, devices can be compromised and converted into bots whereby severe DDoS attacks can be launched by a botmaster. Such tiny devices are safe only when connected to networks with defense mechanisms installed in their networking devices like routers and switches, which might not be present everywhere, e.g. on public/free Wi-Fi networks. To safeguard tiny IoT devices from cyberattacks, we provide resource-friendly standalone attack detection models termed Edge2Guard (E2G) that enable MCU-based IoT devices to instantly detect IoT attacks without depending on networks or any external protection mechanisms. During evaluation, our top-performing E2G models detected and classified ten types of Mirai and Bashlite malware with close to 100% detection rates.

/pdf/edge2guard-botnet-attacks-detecting-offline-models-for-4etp3n8sye.pdf

Muhammad Intizar Ali

Papers

Towards Scalable Non-Monotonic Stream Reasoning via Input Dependency Analysis

QoS-Aware Stream Federation and Optimization Based on Service Composition

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: demo abstract

Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices