6 research outputs found
Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction
The choice of architecture of artificial neuron network (ANN) is still a
challenging task that users face every time. It greatly affects the accuracy of
the built network. In fact there is no optimal method that is applicable to
various implementations at the same time. In this paper we propose a method to
construct ANN based on clustering, that resolves the problems of random and ad
hoc approaches for multilayer ANN architecture. Our method can be applied to
regression problems. Experimental results obtained with different datasets,
reveals the efficiency of our method
Prediction of the transaction confirmation time in Ethereum Blockchain
La blockchain propose un systĂšme d'enregistrement dĂ©centralisĂ©, immuable et transparent. Elle offre un rĂ©seau de nĆuds sans entitĂ© de gouvernance centralisĂ©e, ce qui la rend "indĂ©chiffrable" et donc plus sĂ»r que le systĂšme d'enregistrement centralisĂ© sur papier ou centralisĂ© telles que les banques. Lâapproche traditionnelle basĂ©e sur lâenregistrement ne fonctionne pas bien avec les relations numĂ©riques oĂč les donnĂ©es changent constamment. Contrairement aux canaux traditionnels, rĂ©gis par des entitĂ©s centralisĂ©es, blockchain offre Ă ses utilisateurs un certain niveau d'anonymat en leur permettant d'interagir sans divulguer leur identitĂ© personnelle et en leur permettant de gagner la confiance sans passer par une entitĂ© tierce.
En raison des caractĂ©ristiques susmentionnĂ©es de la blockchain, de plus en plus d'utilisateurs dans le monde sont enclins Ă effectuer une transaction numĂ©rique via blockchain plutĂŽt que par des canaux rudimentaires. Par consĂ©quent, nous devons de toute urgence mieux comprendre comment ces opĂ©rations sont gĂ©rĂ©es par la blockchain et combien de temps cela prend Ă un nĆud du rĂ©seau pour confirmer une transaction et lâajouter au rĂ©seau de la blockchain.
Dans cette thĂšse, nous visons Ă introduire une nouvelle approche qui permettrait d'estimer le temps il faudrait Ă un nĆud de la blockchain Ethereum pour accepter et confirmer une transaction sur un bloc tout en utilisant l'apprentissage automatique. Nous explorons deux des approches les plus fondamentales de lâapprentissage automatique, soit la classification et la rĂ©gression, afin de dĂ©terminer lequel des deux offrirait lâoutil le plus efficace pour effectuer la prĂ©vision du temps de confirmation dans la blockchain Ethereum. Nous explorons le classificateur NaĂŻve Bayes, le classificateur Random Forest et le classificateur Multilayer Perceptron pour lâapproche de la classification. Comme la plupart des transactions sur Ethereum sont confirmĂ©es dans le dĂ©lai de confirmation moyen (15 secondes) de deux confirmations de bloc, nous discutons Ă©galement des moyens pour rĂ©soudre le problĂšme asymĂ©trique du jeu de donnĂ©es rencontrĂ© avec lâapproche de la classification. Nous visons Ă©galement Ă comparer la prĂ©cision prĂ©dictive de deux modĂšles de rĂ©gression dâapprentissage automatique, soit le Random Forest Regressor et le Multilayer Perceptron, par rapport Ă des modĂšles de rĂ©gression statistique, prĂ©cĂ©demment proposĂ©s, avec un critĂšre dâĂ©valuation dĂ©fini, afin de dĂ©terminer si lâapprentissage automatique offre un modĂšle prĂ©dictif plus prĂ©cis que les modĂšles statistiques conventionnels.Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it âunhackableâ and therefore, more secure than the traditional paper based or centralised system of records like banks etc. While there are certain advantages to the paper based recording approach, it does not work well with digital relationships where the data is in constant flux. Unlike traditional channels, governed by centralized entities, blockchain offers its users a certain level of anonymity by providing capabilities to interact without disclosing their personal identities and allows them to build trust without a third-party governing entity.
Due to the aforementioned characteristics of blockchain, more and more users around the globe are inclined towards making a digital transaction via blockchain than via rudimentary channels. Therefore, there is a dire need for us to gain insight on how these transactions are processed by the blockchain and how much time it may take for a peer to confirm a transaction and add it to the blockchain network.
In this thesis, we aim to introduce a novel approach that would allow one to estimate the time (in block time or otherwise) it would take for Ethereum Blockchain to accept and confirm a transaction to a block using machine learning. We explore two of the most fundamental machine learning approaches, i.e., Classification and Regression in order to determine which of the two would be more accurate to make confirmation time prediction in the Ethereum blockchain. More specifically, we explore NaĂŻve Bayes classifier, Random Forest classifier and Multilayer Perceptron classifier for the classification approach. Since most transactions in the network are confirmed well within the average confirmation time of two block confirmations or 15 seconds, we also discuss ways to tackle the skewed dataset problem encountered in case of the classification approach. We also aim to compare the predictive accuracy of two machine learning regression models- Random Forest Regressor and Multilayer Perceptron against previously proposed statistical regression models under a set evaluation criterion; the objective is to determine whether machine learning offers a more accurate predictive model than conventional statistical models
DATA-DRIVEN ANALYSIS AND MAPPING OF THE POTENTIAL DISTRIBUTION OF MOUNTAIN PERMAFROST
In alpine environments, mountain permafrost is defined as a thermal state of the ground and it corresponds to any lithosphere material that is at or below 0°C for at least two years. Its degradation is potentially leading to an increasing rock fall activity and sediment transfer rates. During the last 20 years, knowledge on this phenomenon has significantly improved thanks to many studies and monitoring projects, revealing an extremely discontinuous and complex spatial distribution, especially at the micro scale (scale of a specific landform; tens to several hundreds of metres).
The objective of this thesis was the systematic and detailed investigation of the potential of data-driven techniques for mountain permafrost distribution modelling. Machine learning (ML) algorithms are able to consider a greater number of pa- rameters compared to classic approaches. Not only can permafrost distribution be modelled by using topo-climatic parameters as a proxy, but also by taking into ac- count known field permafrost evidences. These latter were collected in a sector of the Western Swiss Alps and they were mapped from field data (thermal and geoelectrical data) and ortho-image interpretations (rock glacier inventorying). A permafrost dataset was built from these evidences and completed with environmental and mor- phological predictors. Data were firstly analysed with feature relevance techniques in order to identify the statistical contribution of each controlling factor and to exclude non-relevant or redundant predictors. Five classification algorithms, belonging to statistics and machine learning, were then applied to the dataset and tested: Logistic regression (LR), linear and non-linear Support Vector Machines (SVM), Multilayer perceptrons (MLP) and Random forests (RF). These techniques inferred a classifica- tion function from labelled training data (pixels of permafrost absence and presence) to predict the permafrost occurrence where this was unknown.
Classification performances, assessed with AUROC curves, ranged between 0.75 (linear SVM) and 0.88 (RF). These values are generally indicative of good model performances. Besides these statistical measures, a qualitative evaluation was performed by using field expert knowledge. Both quantitative and qualitative evaluation approaches suggested to employ the RF algorithm to obtain the best model. As machine learning is a non-deterministic approach, an overview of the model uncertainties is also offered. It informs about the location of most uncertain sectors where further field investigations are required to be carried out to improve the reliability of permafrost maps.
RF demonstrated to be efficient for permafrost distribution modelling thanks to consistent results that are comparable to the field observations. The employment of environmental variables illustrating the micro-topography and the ground charac- teristics (such as curvature indices, NDVI or grain size) favoured the prediction of the permafrost distribution at the micro scale. These maps presented variations of probability of permafrost occurrence within distances of few tens of metres. In some talus slopes, for example, a lower probability of occurrence in the mid-upper part of the slope was predicted. In addition, permafrost lower limits were automatically recognized from permafrost evidences. Lastly, the high resolution of the input dataset (10 metres) allowed elaborating maps at the micro scale with a modelled permafrost spatial distribution, which was less optimistic than traditional spatial models. The permafrost prediction was indeed computed without recurring to altitude thresh- olds (above which permafrost may be found) and the representation of the strong discontinuity of mountain permafrost at the micro scale was better respected.
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Dans les environnements alpins, le pergĂ©lisol de montagne est dĂ©fini comme un Ă©tat thermique du sol et correspond Ă tout matĂ©riau de la lithosphĂšre qui maintient une tempĂ©rature Ă©gale ou infĂ©rieure Ă 0°C pendant au moins deux ans. Sa dĂ©gradation peut conduire Ă une activitĂ© croissante de chutes de blocs et Ă une augmentation des taux de transfert de sĂ©diments. Au cours des 20 derniĂšres annĂ©es, les connaissances sur ce phĂ©nomĂšne ont considĂ©rablement augmentĂ© grĂące Ă de nombreuses Ă©tudes et projets de suivi, qui ont rĂ©vĂ©lĂ© une distribution spatiale extrĂȘmement discontinue et complexe du phĂ©nomĂšne, en particulier Ă la micro-Ă©chelle (Ă©chelle dâune forme gĂ©omorphologique; dizaines Ă plusieurs centaines de mĂštres).
Lâobjectif de cette recherche Ă©tait lâĂ©tude systĂ©matique et dĂ©taillĂ©e des potentialitĂ©s offertes par une approche axĂ©e sur les donnĂ©es dans le cadre de la modĂ©lisation de la distribution du pergĂ©lisol de montagne. Les algorithmes dâapprentissage au- tomatique (machine learning) sont capables de considĂ©rer un plus grand nombre de variables que les approches classiques. La distribution du pergĂ©lisol peut ĂȘtre modĂ©lisĂ©e non seulement en utilisant des paramĂštres topo-climatiques (altitude, radiation solaire, etc.), mais aussi en tenant compte de la prĂ©sence et de lâabsence connues du pergĂ©lisol (observations de terrain). CollectĂ©es dans un secteur des Alpes occidentales suisses, ces derniĂšres ont Ă©tĂ© cartographiĂ©es sur la base dâinvestigations de terrain (donnĂ©es thermiques et gĂ©oĂ©lectriques), dâinterprĂ©tation dâorthophotos et dâinventaires de glaciers rocheux. Un jeu de donnĂ©es a Ă©tĂ© construit Ă partir de ces Ă©vidences de terrain et complĂ©tĂ© par des prĂ©dicteurs environnementaux et morphologiques. Les donnĂ©es ont dâabord Ă©tĂ© analysĂ©es avec des techniques mon- trant la pertinence des variables permettant dâidentifier la contribution statistique de chaque facteur de contrĂŽle et dâexclure les prĂ©dicteurs non pertinents ou redondants. Cinq algorithmes de classification appartenant aux domaines des statistiques et de lâapprentissage automatique ont ensuite Ă©tĂ© appliquĂ©s et testĂ©s : Logistic regression (LR), la version linĂ©aire et non-linĂ©aire de Support Vector Machines (SVM), Mul- tilayer perceptrons (MLP) et Random forests (RF). Ces techniques dĂ©duisent une fonction de classification Ă partir des donnĂ©es dites dâentraĂźnement reprĂ©sentant lâabsence et la prĂ©sence certaine du pergĂ©lisol. Elles permettent ensuite de prĂ©dire lâoccurrence du phĂ©nomĂšne lĂ oĂč elle est inconnue.
Les performances de classification, Ă©valuĂ©es avec des courbes AUROC, variaient entre 0.75 (SVM linĂ©aire) et 0.88 (RF). Ces valeurs sont gĂ©nĂ©ralement indicatives de bonnes performances. En plus de ces mesures statistiques, une Ă©valuation qualitative a Ă©tĂ© rĂ©alisĂ©e et se base sur lâexpertise gĂ©omorphologique. Les RF se sont rĂ©vĂ©lĂ©es ĂȘtre la technique produisant le meilleur modĂšle. Comme lâapprentissage automatique est une approche non dĂ©terministe, il a Ă©galement offert un aperçu des incertitudes de la modĂ©lisation, qui informent sur la localisation des secteurs les plus incertains dans lesquels des futures campagnes de terrain mĂ©ritent dâĂȘtre menĂ©es afin dâamĂ©liorer la fiabilitĂ© des cartes produites.
Finalement, RF ont dĂ©montrĂ© leur efficacitĂ© dans le cadre de la modĂ©lisation de la distribution du pergĂ©lisol grĂące Ă des rĂ©sultats comparables aux observations de terrain. Lâemploi de variables environnementales illustrant la micro-topographie du relief et les caractĂ©ristiques du sol (tels que les indices de courbure, le NDVI et la granulomĂ©trie) favorise la prĂ©diction de la distribution du pergĂ©lisol Ă la micro- Ă©chelle, avec des cartes prĂ©sentant des variations de la probabilitĂ© dâoccurrence du pergĂ©lisol sur des distances de quelques dizaines de mĂštres. Par exemple, dans cer- tains Ă©boulis, les cartes illustrent une probabilitĂ© plus faible dans la partie amont de la pente, ce qui sâavĂšre cohĂ©rent avec les observations de terrain. La limite infĂ©rieure du pergĂ©lisol a ainsi Ă©tĂ© automatiquement reconnue Ă partir des Ă©vidences de terrain fournies Ă lâalgorithme. Enfin, la haute rĂ©solution du jeu de donnĂ©es (10 mĂštres) a permis dâĂ©laborer des cartes prĂ©sentant une distribution spatiale du pergĂ©lisol moins optimiste que celle offerte par les modĂšles spatiaux classiques. La prĂ©diction du pergĂ©lisol a en effet Ă©tĂ© calculĂ©e sans utiliser des seuils dâaltitude (au-dessus desquels on peut trouver du pergĂ©lisol) et respecte ainsi mieux la reprĂ©sentation de la forte discontinuitĂ© du pergĂ©lisol de montagne Ă la micro-Ă©chelle.
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Negli ambienti alpini, il permafrost di montagna Ăš definito come uno stato termico del suolo e corrisponde a qualsiasi materiale nella litosfera che mantiene una temper- atura uguale o inferiore a 0° C per almeno due anni. La sua degradazione puĂČ portare ad una crescente attivitĂ di caduta di blocchi e ad un aumento dei tassi di trasferi- mento dei sedimenti. Negli ultimi 20 anni, le conoscenze riguardanti il permafrost di montagna sono aumentate considerevolmente grazie ai numerosi studi e progetti di monitoraggio che hanno rivelato una distribuzione spaziale fortemente discontinua e complessa del fenomeno, in particolare alla scala della forma geomorfologica (definita come la micro scala, da decine a diverse centinaia di metri).
Lâobiettivo di questa ricerca Ă© lo studio sistematico e dettagliato delle potenzialitĂ offerte da un approccio basato sui dati, nellâottica di una modellizzazione della distribuzione del permafrost di montagna. Gli algoritmi di apprendimento auto- matico (machine learning) sono in grado di considerare piĂč variabili rispetto agli approcci classici. La distribuzione del permafrost puĂČ essere modellizzata non solo utilizzando i parametri topo-climatici classici (altitudine, radiazione solare, ecc.), ma anche considerando esempi di presenza e assenza del permafrost (osservazioni sul campo). Raccolti in unâarea delle Alpi occidentali svizzere, questi ultimi sono stati mappati sulla base di indagini di terreno (dati termici e geoelettrici), interpretazione di ortofoto e inventari di ghiacciai rocciosi. A partire dalle evidenze di terreno, Ăš stato creato un set di dati, al quale sono stati integrati diversi predittori ambien- tali e morfologici. I dati sono stati dapprima analizzati con tecniche di indagine della rilevanza delle variabili; tali tecniche sono capaci di identificare il contributo statistico di ciascun fattore di controllo del permafrost e sono in grado di escludere i predittori non pertinenti o ridondanti. Sono stati, quindi, applicati e testati cinque al- goritmi di classificazione appartenenti ai campi della statistica e dellâapprendimento automatico: Logistic regression (LR), la versione lineare e non lineare di Support Vector Machines (SVM), Multilayer Perceptron (MLP) e Random forest (RF). Queste tecniche deducono una funzione di classificazione dai cosiddetti dati di allenamento, che rappresentano lâassenza e la presenza certa del permafrost, e permettono in seguito di predire il fenomeno laddove Ăš sconosciuto.
Le prestazioni di classificazione, valutate con le curve AUROC, variavano da 0.75 (SVM lineare) a 0.88 (RF). Questi valori sono generalmente indicativi di buone prestazioni. Oltre a queste misure statistiche, Ăš stata effettuata una valutazione qualitativa. RF si Ă© rivelata essere la tecnica che produce il modello migliore. PoichĂ© lâapprendimento automatico Ăš un approccio non deterministico, Ă© stato possibile ottenere informazioni sulle incertezze della modellizzazione. Questâultime indicano in quali aree il modello Ă© piĂč incerto e, dunque, dove occorre pianificare nuove campagne di terreno per migliorare lâaffidabilitĂ delle mappe prodotte.
RF ha dimostrato la sua efficacia nella modellizzazione della distribuzione del per- mafrost con risultati paragonabili alle osservazioni sul campo. Lâuso di variabili ambientali che illustrano la topografia e le caratteristiche del suolo (come indici di curvatura, NDVI e granulometria) aiuta a predire la distribuzione del permafrost alla micro scala, con mappe che mostrano variazioni spaziali importanti della probabilitĂ del permafrost su distanze di poche decine di metri. In alcune falde di detrito le mappe mostrano una probabilitĂ inferiore nella parte a monte, risultato coerente con le osservazioni sul campo. Il limite inferiore del permafrost Ăš stato inoltre riconosci- uto automaticamente dagli esempi forniti allâalgoritmo. Infine, lâalta risoluzione del set di dati (10 metri) ha permesso una simulazione della distribuzione spaziale del fenomeno meno ottimistica rispetto a quella fornita dai modelli classici. La previsione del permafrost Ăš stata, infatti, calcolata senza utilizzare delle soglie di altitudine e quindi rispetta meglio la rappresentazione dellâalta discontinuitĂ del permafrost di montagna alla micro scala
Full Proceedings, 2018
Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University
Securing IoT Applications through Decentralised and Distributed IoT-Blockchain Architectures
The integration of blockchain into IoT can provide reliable control of the IoT network's
ability to distribute computation over a large number of devices. It also allows the AI
system to use trusted data for analysis and forecasts while utilising the available IoT
hardware to coordinate the execution of tasks in parallel, using a fully distributed
approach.
This thesis's ârst contribution is a practical implementation of a real world IoT-
blockchain application,
ood detection use case, is demonstrated using Ethereum proof
of authority (PoA). This includes performance measurements of the transaction con-
ârmation time, the system end-to-end latency, and the average power consumption.
The study showed that blockchain can be integrated into IoT applications, and that
Ethereum PoA can be used within IoT for permissioned implementation. This can be
achieved while the average energy consumption of running the
ood detection system
including the Ethereum Geth client is small (around 0.3J).
The second contribution is a novel IoT-centric consensus protocol called honesty-
based distributed proof of authority (HDPoA) via scalable work. HDPoA was analysed
and then deployed and tested. Performance measurements and evaluation along with
the security analyses of HDPoA were conducted using a total of 30 diâerent IoT de-
vices comprising Raspberry Pis, ESP32, and ESP8266 devices. These measurements
included energy consumption, the devices' hash power, and the transaction conârma-
tion time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J,
54Kh/J, and 22.4Kh/J when using the Raspberry Pi, the ESP32 devices, and the
ESP8266 devices, respectively, this achieved while there is limited impact on each de-
vice's power. In HDPoA the transaction conârmation time was reduced to only one
block compared to up to six blocks in bitcoin.
The third contribution is a novel, secure, distributed and decentralised architecture
for supporting the implementation of distributed artiâcial intelligence (DAI) using
hardware platforms provided by IoT. A trained DAI system was implemented over the
IoT, where each IoT device hosts one or more neurons within the DAI layers. This
is accomplished through the utilisation of blockchain technology that allows trusted
interaction and information exchange between distributed neurons. Three diâerent
datasets were tested and the system achieved a similar accuracy as when testing on a
standalone system; both achieved accuracies of 92%-98%. The system accomplished
that while ensuring an overall latency of as low as two minutes. This showed the secure architecture capabilities of facilitating the implementation of DAI within IoT
while ensuring the accuracy of the system is preserved.
The fourth contribution is a novel and secure architecture that integrates the ad-
vantages oâered by edge computing, artiâcial intelligence (AI), IoT end-devices, and
blockchain. This new architecture has the ability to monitor the environment, collect
data, analyse it, process it using an AI-expert engine, provide predictions and action-
able outcomes, and ânally share it on a public blockchain platform. The pandemic
caused by the wide and rapid spread of the novel coronavirus COVID-19 was used as
a use-case implementation to test and evaluate the proposed system. While providing
the AI-engine trusted data, the system achieved an accuracy of 95%,. This is achieved
while the AI-engine only requires a 7% increase in power consumption. This demon-
strate the system's ability to protect the data and support the AI system, and improves
the IoT overall security with limited impact on the IoT devices.
The âfth and ânal contribution is enhancing the security of the HDPoA through
the integration of a hardware secure module (HSM) and a hardware wallet (HW). A
performance evaluation regarding the energy consumption of nodes that are equipped
with HSM and HW and a security analysis were conducted. In addition to enhancing
the nodes' security, the HSM can be used to sign more than 120 bytes/joule and
encrypt up to 100 bytes/joule, while the HW can be used to sign up to 90 bytes/joule
and encrypt up to 80 bytes/joule. The result and analyses demonstrated that the HSM
and HW enhance the security of HDPoA, and also can be utilised within IoT-blockchain
applications while providing much needed security in terms of conâdentiality, trust in
devices, and attack deterrence.
The above contributions showed that blockchain can be integrated into IoT systems.
It showed that blockchain can successfully support the integration of other technolo-
gies such as AI, IoT end devices, and edge computing into one system thus allowing
organisations and users to beneât greatly from a resilient, distributed, decentralised,
self-managed, robust, and secure systems
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
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annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma