Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomic Classification of Media Reports in the Cyber Attack Domain

Cyber-attacks have become a huge threat to the information age. In a previous study, cyber-attacks associated with events in Social, Political, Economic and Cultural (SPEC) dimensions were analyzed [6]. The task of this research is to construct an automated classifier that can classify media reports related to past and current cyber-attack events according to the SPEC taxonomy. The classifier was built on a machine learning principle incorporated with approaches focused on 1) document indexing; 2) calculation of classification thresholds; 3) definition of classification effectiveness; and 4) calculation of precision and recall. The classifier is expected to perform with acceptable effectiveness so that it helps analysts to deal with large number of media reports that are predictive of possible cyber-attacks.
Calculating Pascal’s Principle in a Non-Hearing Grade 9 Technology Classroom How Do Deaf Learners use Sign Language to Enhance Calculation in Hydraulics

Education is a constitutional right of all children granted to improve the quality of life for all citizen and the condition under which they live, prioritising Technology and scientific subjects for Technology literacy through English as a medium of instruction. Deaf learners engaged with these subject through Sign Language that has not been officiated for mediating learning in education. This paper aims at understanding the comprehension of concept formulation and breakdown in Deaf learners when they engage with hydraulic systems’ calculation if properties in concept formulation follow Mathematics procedure for numbers. Moreover it explored the utilization of Sign Language to enhance calculations in hydraulic system. This study is guided by constructivism and social constructivism theory as participants were building on their knowledge and experiences as well as interactive learning for effective learning. This was a case study since the research was done in one grade 9 Technology classroom where five Deaf learners where purposely chosen in the enquiry. Participant observation, document analyses and interviews were used within the qualitative mean to develop and analyse data that aided the researcher to comprehend the perspectives held by participants. Participants had good retention ability to concepts they engaged with through Sign Language. When tasks involve single stimulus, participants were able to recall related aspects. Lastly participant encountered challenges on tasks that depended on integrated knowledge from multi-dimensional. Participants should engage actively. Keywords— Sign Language; Deaf learners; Mechanical Systems; Pascal’s principle; nterdisciplinary
Approximate Computing in Coarse Grained Reconfigurable Architecture

Approximate computing has emerged as a new computing paradigm capable of reducing the power requirements for or accelerating some workloads. Due to cascading error and the nature of binary arithmetic, it is difficult to predict the exact effects that approximation may have on an error tolerant workload. In this work, we implemented configurable levels of approximation into a Coarse Grained Reconfigurable Architecture (CGRA) to study the effects of error tolerant algorithms on an approximate CGRA. We will use the CGRA Compilation Framework which simulates a CGRA using gem5, and we will implement the approximate hardware using multiple different approximate arithmetic modules included in Low Power Approximate Computing Library. Finally, we will perform a hardware level simulation on approximate modules to estimate the reduction in power from using approximate hardware.
Energy Minimization for D2D-Assisted Mobile Edge Computing Networks

This paper addresses the energy minimization problem in Device-to-Device (D2D) assisted Mobile Edge Computing (MEC) networks under the latency constraint of each individual task and the computing resource constraint of each computing entity. The energy minimization problem is formed as a two-stage optimization problem. Specifically, in the first stage, an initial feasibility problem is formed to maximize the number of executed tasks and the global energy minimization problem is tackled in the second stage while maintaining the maximum number of executed tasks. Both of the optimization problems in two stages are NP-hard, therefore a low-complexity algorithm is developed for the initial feasibility problem with a supplementary algorithm further proposed for energy minimization. Simulation results demonstrate the near-optimal performance of the proposed algorithms and the fact that with the assistance of D2D communication, the number of executed tasks is greatly increased and the energy consumption per executed task is significantly reduced in MEC networks, especially in dense user scenario.
The reasoning and learning Lab Chatbot: a solution to the conversational intelligence challenge

Over the years, the richness of human computer interaction has greatly evolved. As a result, it is now possible to command a computer, phone, or watch using natural language. Recently, the idea of having a text-based conversation with a machine became more feasible. This is in particular due to modern developments in Deep Learning, a quickly growing area of research in the field of Artificial Intelligence that has achieved impressive results in various applications such as conversational intelligence.Current dialog systems are able to understand our queries and do simple tasks such as listing the news, obtaining the weather or playing music. A significant amount of efforts have been made to train such systems. A logical next step is to be able to have a relatively long and entertaining conversation with chatbots in order to solve more complex tasks. Competitions like the Conversational Intelligence (ConvAI) challenge are being organized to push the research development towards that goal.This thesis undertakes an analysis of modern dialog systems that use deep learning tools. More precisely, we present in details the Reasoning and Learning Lab Chatbot (RLLChatBot) that participated in the ConvAI challenge. We analyze different text generation models and the important role of a ranking system in order to regulate the conversation flow. The main contribution of this work is that we provide a thorough description of how a dialog system can be built and trained from mostly public-domain datasets using Deep Learning tools. Moreover, we present an additional dataset we collected, allowing to improve our ranking system that evaluates which candidate response should be returned to the user at each time step in the conversation. We leave as future work the goal of unifying the entire pipeline we present, as of now, each model is trained independently. %%%%Au fil des ans, la richesse de l'interaction homme-machine a beaucoup evolue. Par consequent, il est maintenant possible de commander un ordinateur, un telephone ou une montre en utilisant le langage naturel. Recemment, l'idee d'avoir une conversation avec une machine est devenue realisable. Ceci est du en particulier aux developpements modernes d'apprentissage profond, une dicipline de recherche en croissance rapide dans le domaine de l'intelligence artificielle qui a obtenu des resultats impressionnants dans diverses applications telles que l'intelligence conversationnelle.Les systemes de dialogue actuels sont capables de comprendre nos requetes et de faire des tâches simples telles que lister les nouvelles, obtenir la meteo ou jouer de la musique. Des efforts importants ont ete deployes pour former de tels systemes. Une prochaine etape logique est de pouvoir avoir une conversation relativement longue et divertissante avec les chatbots afin de resoudre des tâches plus complexes. Des competitions comme le Conversational Intelligence (ConvAI) challenge sont organisees pour pousser le developpement de la recherche vers cet objectif.Cette these entreprend une analyse des…
Comparative Study on Face Image Segmentation Using Various Edge Detection Techniques

Image Segmentation plays an important role in image processing and computer vision. Segmentation subdivides an image into its constituent regions or object. Image segmentation can be obtained by using various methods, some which are easier to achieve than others due to the need of high programming. Edge detection techniques are mostly used to find object based on local changes in intensity field of image processing applications. In this paper attempt to study the most commonly used edge detection techniques such as canny, Sobel, Prewitt, Roberts, LoG. From the experiments and results observed the canny edge detection gives better results.
Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

The last few decades have witnessed various approaches to automate the process of plant classification using the characteristics of the leaf. Several approaches have been proposed, and the majority focused on global shape features. However, one challenge that faces this task is the high interclass similarity amongst the leaves of different species in terms of the global shape. Furthermore, there always has been an obstacle against full automation as several approaches require user intervention to align the leaf. Therefore, a new set of Quartile Features (QF) is proposed in this paper to describe the partial shape of the leaf, in addition to an automated alignment approach to automate the system. The QF are extracted from the horizontal and vertical leaf quartiles to describe the partial shape of the leaf and the relations among its parts. The well-known Flavia dataset has been selected for the evaluation of the proposed system. The experimental results indicate the ability of the proposed alignment algorithm to align leaves with different shapes and maintain a correct classification accuracy regardless of the orientation of the input leaf samples. Furthermore, the proposed QF indicated promising results by increasing the accuracy of the classification by a range of approximately 26% to 30% when combined with Hu’s Moment Invariants, using k-fold cross-validation technique.
Regulating best effort flows on per domain basis

It is difficult to properly regulate best effort flows. Although several methods have been proposed, the communication cost is too high to make them effective for large number of non-TCP friendly flows. We solve this problem by making a tradeoff between the communication cost and the computation complexity of core routers. In addition, core routers and edge routers regulate aggregate flows without dynamic calculation of round trip time and consumption of extra buffers. By simulation experiments, we find our proposal is effective in both tail-drop FIFO queuing and RED queuing.
Modeling and control of a biomimetic underwater vehicle

Biomimetic underwater vehicles have recently become a focus of academic research. They offer possibly better maneuverability and higher efficency than conventional thrusters and the potential for amphibious capability. The focus of this thesis is the modeling and control of biomimetic underwater vehicles, and the specific platform used for experimentation is the Aqua underwater vehicle. The study begins with the development of a dynamics model for the Aqua underwater vehicle. An existing model was updated to account for the exibility of the oscillating paddles. The model was validated experimentally and linearized to enable the design of more advanced controllers. Based on the paddle model, we also develop a reverse model/mapping that determines the paddle motion needed to produce a desired thrust. We design four classes of trajectory tracking controllers: PID, model-based controllers, adaptive controller and Floquet controller. The controllers were first tested in the dynamics simulation to tune the control gains and assess their performance. We found that the adaptive and Floquet controllers were superior to the other two. The adaptive controller was more accurate to track a changing trajectory while the Floquet was better in set point tracking. The controllers were then tested experimentally on the actual Aqua vehicle. The simulation results were partially corroborated but the controllers were not as accurate. The adaptive controller gave better performance in pitch while the Floquet controller was more accurate in roll. An optimization of a U-turn was performed to improve the performance of the vehicle in coral reef inspection. Three design variables were chosen for the optimization: the turn radius, the speed and the bank angle. The turn radius and speed had the most impact on the performance while the bank angle had little effect. %%%%Les vehicules sous-marins biomimetiques sont recemment devenus un domaine de recherche populaire dans les milieux academiques. Ils offrent entres autres la possibilite d'une meilleure maneuvrabilite, une plus grande efficacite et des capacites amphibies. Cette these se concentre sur la modelisation et les commandes de vehicules sous-marins biomimetiques. Le vehicule sous-marin Aqua est utilise comme plateforme d'experimentation. Nos recherches ont commence par le developpement d'un modele dynamique pour le vehicule sous-marin Aqua. Le modele fut valide experimentalement puis linearise afin de permettre la conception de commandes avancees. Un second modele, dit inverse, permettant de determiner le mouvement d'une nageoire en fonction de la poussee desiree fut par la suite developpe. Nous avons concu quatre classes de commandes: PID, commandes predictives, commandes adaptatives et Floquet. Ces commandes furent d'abord testees a l'aide d'une simulation informatique afin d'ajuster les gains et d'evaluer la performance des commandes. Nous avons decouvert que les commandes adaptives et Floquet etaient superieures aux deux autres. Les commandes adaptatives etaient plus precises lorsque…
Fusion of inertial data in industrial Automated Guided Vehicles

Automated guided vehicles are a key factor in the optimization of the transportation of materials in the industry sector. The interest of these vehicles is directly related with their capability of carrying heavy loads among the different steps of an assembly line, repetitively, while demanding a reduced human intervention. The navigation of these vehicles is often based on optical or magnetic guidance. However, the inflexibility of guidelines combined with its deterioration over time leads to the development of alternative or complementary methods of navigation for these vehicles. The main objective of this work is to develop and validate an inertial-based guidance system, composed of accelerometers and gyroscopes, in order to complement an existing magnetic-based guidance system. The inertial-based guided system should be capable of guiding the vehicle, allowing its navigation along straight paths in which the magnetic tape is not available. For this task, the design and assembly of an inertial data acquisition system is firstly performed, followed by a study of sensor fusion algorithms as well as their implementation in embedded systems. Standard accurate fusion algorithms implemented to merge sensor measurements and mitigate existing errors are computationally intensive, demanding an high computational capability which is incompatible with low-cost devices. Therefore, this work focus on low-complexity fusion filter approaches, which are capable of merging low-cost inertial sensors data and which can be implemented in embedded systems with low-computational capabilities. The results demonstrate that the inertial-based guidance system properly emulate the behaviour of the magnetic-guided system, for straight paths. This work is included in the AGVPOSYS (Automated Guided Vehicle with innovative indoor POsitioning SYStem for the factory of the future) project led by the company Active Space Technologies and was developed in the facilities of this company.
The computer security framework

The organization and maintenance of a security framework constitutes a challenge for every company. The author describes the element of such a framework: objectives, conditions, measures and rules which in nature may be organizational, technical, personal, etc. Security guidelines are presented for physical security, security against water damage, lightning and overvoltage protection, protection of the power supply, and disaster recovery. <<ETX>>
Impact of Feature Representation for Imitation Learning in Autonomous Drive

Autonomous drive in complex traffic scenarios is a demanding task to solve. With high-dimensional input data available, problems related to redundancy and irrelevance are often implicated, hence determining what features bring the most useful information is of vital importance. The purpose of this thesis is to investigate how different dimensionality reduction methods affect the driving performance and how to determine what features are most relevant. Specifically, these questions were studied in a simulated environment where a car is manoeuvred using deep neural networks through a sequence of signalised intersections. Four different dimensionality reduction methods have been studied: choice of features based on reason, Principal Component Analysis, Auto-Encoders and Integrated Encoders. The results showed that the models which used a feature representation based on reason were shown to perform best. Also, the weight distributions of a model using all available features indicated that influential features may be partially identified by studying the spread of the weights. Therefore, an approach is proposed where the choice of features should be based on reason as well as a study of the features’ respective set of weights. In conclusion, establishing the most relevant feature representation is important since it may benefit the training of the models.
A MACHINE-LEARNING FRAMEWORK FOR AUTOMATING WELL-LOG DEPTH MATCHING

Depth matching well logs acquired from multiple logging passes in a single well has been a long-standing challenge for the industry. The existing approaches employed in commercial platforms are typically based on classical cross-correlation and covariance measures of two signals, followed by manual adjustments. These solutions do not satisfy the rising demand to minimize user intervention to proceed towards automated data interpretation. We aimed at developing a robust and fully automatic algorithm and workflow for depth matching gamma-ray logs, which are commonly used as a proxy to match the depth of other well logs measured in multiple logging passes within the same well. This was realized by a supervised machine learning approach through a fully connected neural network. The training dataset was obtained by manually labeling a limited set of field data. As it is unrealistic to expect a perfect model from the initial training with limited manually labeled data, we developed a continuously self-evolving depth matching framework. During the use of depth matching service, this framework allows taking the user input and feedback to further train and improve the depth matching engines. This is facilitated by an automatic quality-control module for that we developed a dedicated metric by combining a few different algorithms. We utilize this metric to assess the quality of the returned results from the depth matching engine. The users review the results and do manual adjustments if some intervals are not ideally depth-matched by the engine. Those manual adjustments are sent back to the database, and the framework re-trains the machine learning model based on the updated database. The newly trained model can be validated and compared against existing models using the developed metrics system. Enhanced models are committed into the model pool that is part of the service. As the users keep using the service, the depth matching framework is anticipated to automatically and continuously improve in performance which eventually leads to an optimal and fully automated depth matching workflow. A key aspect of the developed framework is its generalization potential because it is agnostic of the signal type. It could be easily extended for other log types, especially when the correlation thereof is not obvious, provided that a sufficiently large volume of labeled data is available. This framework has been prototyped and tested on field data.
Control and preprocessing of graphic data for effective dynamic object recognition

The features of detecting static objects are considered. The methods of Sparse Feature Propagation and Dense Feature Aggregation based on the neural network approach of multi-frame end-to-end learning are described. The methods are designed to increase the efficiency of dynamic object recognition. The general structure of the video analytics system is presented. The method of increasing the accuracy of the obtained recognition results and increasing the probability of detecting high-speed objects by the built-in motion detection module is proposed.
Multilingual Abstractions: Abstract Syntax Trees and Universal Dependencies

This thesis studies the connections between parsing friendly representations and interlingua grammars developed for multilingual language generation. Parsing friendly representations refer to dependency tree representations that can be used for robust, accurate and scalable analysis of natural language text. Shared multilingual abstractions are central to both these representations. Universal Dependencies (UD) is a framework to develop cross-lingual representations, using dependency trees for multlingual representations. Similarly, Grammatical Framework (GF) is a framework for interlingual grammars, used to derive abstract syntax trees (ASTs) corresponding to sentences. The first half of this thesis explores the connections between the representations behind these two multilingual abstractions. The first study presents a conversion method from abstract syntax trees (ASTs) to dependency trees and present the mapping between the two abstractions – GF and UD – by applying the conversion from ASTs to UD. Experiments show that there is a lot of similarity behind these two abstractions and our method is used to bootstrap parallel UD treebanks for 31 languages. In the second study, we study the inverse problem i.e. converting UD trees to ASTs. This is motivated with the goal of helping GF-based interlingual translation by using dependency parsers as a robust front end instead of the parser used in GF. The second half of this thesis focuses on the topic of data augmentation for parsing – specifically using grammar-based backends for aiding in dependency parsing. We propose a generic method to generate synthetic UD treebanks using interlingua grammars and the methods developed in the first half. Results show that these synthetic treebanks are an alternative to develop parsing models, especially for under-resourced languages without much resources. This study is followed up by another study on out-of-vocabulary words (OOVs) – a more focused problem in parsing. OOVs pose an interesting problem in parser development and the method we present in this paper is a generic simplification that can act as a drop-in replacement for any symbolic parser. Our idea of replacing unknown words with known, similar words results in small but significant improvements in experiments using two parsers and for a range of 7 languages.
A Multibody Model of an Ornithopter

and feedback control techniques. Analysis of inertial properties and previous ight data has led to the development of a multibody model, where the ornithopter is modeled as a collection of chains of rigid body linkages emanating from a central fuselage. The Boltzmann-Hamel equations of motion are employed to generate a state space model which is cast into a form canonical to the nonlinear robotics and adaptive control communities. Software implementation of the symbolic derivation and numerical integration of the equations of motion are discussed. Preliminary results are presented for the simulation of the ornithopter in straight and level ight using a simplied aerodynamics model, which illustrate the trends in measured ight data.