Miracle-dz/UniFAR-FTUs huggingface dataset:
Lattice boltzmann simulation of laminar flow in a three-dimensional two-sided lid-driven cavity

The present paper computes the laminar flow in a three-dimensional two-sided lid-driven cubical cavity by lattice Boltzmann method. It is known that, the development of Lattice Boltzmann Method is the promising method that uses different kind of non-conventional techniques for applications in computational fluid dynamics. In the present work, the performance and accuracy of the code is first ascertained by computing the flow in a single-sided lid-driven cubical cavity and comparing the results available in the literature. It is found that antiparallel motion of the two-sided lid-driven cubical cavity walls, there appears a single primary vortex with its centre in the centre of the domain. The results for the parallel motion of the walls are also presented in some detail. The problem therefore merits careful investigation, which is attempted in this paper through the lattice Boltzmann method for various Reynolds numbers for the antiparallel and parallel motion of the walls.
IMPLEMENTATION OF HASH FUNCTION FOR CRYPTOGRAPHY ( RSA SECURITY )

In this thesis, a new method for implementing cryptographic hash functions is proposed. This method seeks to improve the speed of the hash function particularly when a large set of messages with similar blocks such as documents with common Headers are to be hashed. The method utilizes the peculiar run-time configurability Feature of FPGA. Essentially, when a block of message that is commonly hashed is identified, the hash value is stored in memory so that in subsequent occurrences of The message block, the hash value does not need to be recomputed; rather it is Simply retrieved from memory, thus giving a significant increase in speed. The System is selflearning and able to dynamically build on its knowledge of frequently Occurring message blocks without intervention from the user. The specific hash Function to which this technique was applied is blake, one of the SHA-3 finalists. Keyword: Hash Function, FPGA, blake, SHA-3
Key challenges for China's carbon emissions trading program

China's national carbon emissions trading program is expected to become the world's largest carbon market and is critical for achieving China's domestic mitigation goals. But China's trading program is likely to face significant challenges, due to its large scale and high complexity. First, the accuracy and credibility of China's emissions data have been widely criticized. There are significant inconsistencies between the national and provincial emissions inventories, and the facility‐level data are even less satisfying. Institutional capacities related to the carbon market need to be significantly enhanced to strengthen monitoring, reporting, and verifying capabilities. Second, compared to emissions trading programs in other parts of the world, China's program is likely to face greater market volatility, due to the country's economic structural transition from a manufacturing‐based to a service‐based economy. Wise cap‐setting and allowance allocation methods and proper price containment mechanisms are needed to maintain market stability. And third, China is a market economy with many features of a central planning system, particularly in the energy sector. Large state‐owned enterprises (SOEs) do not act as profit‐maximizing entities, and their behaviors could undermine the efficiency of the cap‐and‐trade program. To address these challenges, we provide a series of policy recommendations, including capacity building from central to local levels, wise selection of allowance allocation methods to cope with changing economic realities, and deepening market‐oriented reforms in energy sectors and SOEs.
Niche Formation Techniques Based on Coevolutionary Interactions in Multi-Agent System

Niching techniques for evolutionary algorithms are aimed at forming and maintaining stable subpopulations (species) in multimodal domain. This paper reviews some of the existing niching techniques and presents the idea of niching coevolutionary multi-agent system (NCoEMAS). In such a system the niche formation phenomena occurs within one of the preexisting species as a result of coevolutionary interactions. Also the results from runs of NCoEMAS against four commonly used test functions are presented.
Influence of Supply Chain Risk Control Strategies on Performance of Food and Beverage Manufacturing Firms in Kenya

-The food and beverage industry has a special role in expanding economic opportunity yet this growth is threatened by supply chain risks. In this context, adopting proactive strategies is needed for dealing with supply chain risks and vulnerabilities for securing supply chain systems to be responsive and effective. This study focused on finding out the influence of supply chain risk control strategies on supply chain performance of food and beverage manufacturing firms in Kenya. The target population was all food and beverage manufacturing firms in Kenya. The accessible population was 187 food and manufacturing firms drawn from a KAM directory using a census survey method. A five-point Likert scale questionnaire was administered to senior-level managers with the knowledge of supply-chain and logistics functions. Both descriptive and inferential analysis was done using SPSS 17 and structural equation modelling (SEM) R-Lavaan 0.5-20 to find out the influence of supply chain risk control strategies on supply chain performance of food and beverage manufacturing firms in Kenya.
Development of criteria suitable for machine learning based on morphological hierarchical trees A Degree Thesis Submitted to the Faculty of the Escola Tècnica d'Enginyeria de Telecomunicació de Barcelona

1 Resum . 2 Resumen . 3 Acknowledgements . 5 Revision history and approval record . 6 Table of contents . 7 List of Figures . 9 List of Tables: . 10
Self-Supervised Robot Learning

Supervised learning has been used in robotics to solve various tasks like navigation, fine manipulation, etc. While it has shown a promising result, in most cases the supervision comes from the human agent. However, relying on human is a huge bottleneck to scale up these approaches. In this thesis, we try to take the human out of the loop and try to solve the task in a self-supervised manner. More specifically, we have applied self-supervised learning to 2 tasks, for drone navigation and exploration in reinforcement learning. In drone navigation, drone first collides with lots of objects and based on it, it learns a policy to avoid them. In reinforcement learning, we try to learn exploration policy in a self-supervised manner for both stochastic and deterministic environment. How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this work, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. Exploration has been a long standing problem in both model-based and model-free learning methods for sensorimotor control. There have been major advances in recent years demonstrated in noise-free, non-stochastic domains such as video games and simulation. However, most of the current formulations get stuck when there are stochastic dynamics. In this work, we propose a formulation for exploration inspired from the work in active learning literature. Specifically, we train an ensemble of dynamics models and incentivize the agent to maximize the disagreement or variance of those ensembles. We show that this formulation works as well as other
Electron Transport in Graphene-Phagraphene Mixed Structures

The nanoelectronics is today a strong new front of development, providing findings about new properties in a large range of materials. Among different ways to approach nanodevices based technologies there is the molecular electronics [1]. The first design for molecular electronic device was attributed to Aviram and Ratner [2]. New nano materials that have similar properties as conventional electronic devices are the most interesting [3]. Among these materials, graphene based devices have called attention of researchers around the world, due electrical, mechanical and thermal properties and high Fermi velocity in low temperatures [4]. Recently, Z. Wang et al. have suggested a new allotropic form of carbon, the phagraphene, a bi-dimensional material composed by carbon rings with 5-6-7 atoms, with sp2 hybridization and comparable in energy with graphene, but most favorable in energy than other allotropic forms [5]. In this work, we have proposed a phagraphene based nanoelectronic device with metallic graphene leads. To perform electron transport calculations we have used the DFT-NEGF methodology in the Landauer-Büttiker formalism as implemented in the TRANSIESTA [6] code. Properties as current-voltage, transmission through the system compared with molecular orbitals shows interesting results and suggests phagraphene as promising material in nanoelectronics. Key-words: Electron Transport; Graphene; New allotropic forms; Phagraphene;
Bit-Mapping and Channel Coding for Space Modulation

Spatial modulation (SM) is a multi-input-multi-output (MIMO) technique that exploits the channel state information (CSI) to transmit information using only one antenna at a time. Space shift keying (SSK) is a special case of SM, in which only the space domain is used for transmission. The combination of SM/SSK with existing performance enhancement techniques has been studied because of its potential to achieve additional performance gains. In this thesis, we investigate the use of the CSI in SM and SSK in two aspects, namely, the combination of SM/SSK with bit-mapping and with trellis coded modulation (TCM) . In the first part of the thesis, we propose a novel low-complexity adaptive bit mapping scheme for SM called the nearest point method (NPM). In this scheme, the SM symbols are first sorted according to a certain sorting order; then, bit sequences that are different only in one bit are assigned to subsequent SM symbols according to that sorting order. To determine the sorting order, NPM starts with the pair of SM symbols that has the minimum distance. From one of the SM symbols in that pair, it identifies the nearest SM symbol and continues to do so until it visits all the SM symbols without visiting the same SM symbol twice. Simulation results show that NPM achieves performance close to the lower bound of the bit error rate (BER)
Master’s Thesis Master’s Degree in Space and Aeronautical Engineering FROM PURE CONDUCTION TO HOMOGENEOUS ISOTROPIC TURBULENCE

iii Acknowledgements . iii Declaration of honour . iv List of figures . vii List of tables . viii
Integrating a Secure Intranet of Things into a Smart Home Router

The Internet of Things (IoT) is a rapidly growing area of computing, with manufacturers rushing to market and standards bodies sticking to tried and tested architectures. However, when considering an Internet of Things within the home, many of these activities are ill-thought out, inappropriate and even possibly physically dangerous, as evidenced by various attacks [1, 5]. We introduce the concept of an Intranet of Things (iot) by defining a suitable model and device roles to represent the ecosystem of devices typically found within an Intranet of Things. We subsequently present a secure iot protocol implemented on the TelosB mote for TinyOS, which enables users to easily and securely add new Things to the network with minimal configuration, as well as protect the devices, data and user privacy against common attacks. Additionally, we demonstrate integrating our iot protocol into the Homework router Cache, a high-performance complex event processing engine, providing a powerful and customisable closed-loop of control for the iot.
Predicting the accuracy of neural networks from final and intermediate layer outputs

We show that information about whether a neural network’s output will be correct or incorrect is present in the outputs of the network’s intermediate layers. To demonstrate this effect, we train a new ”meta” network to predict from either the final output of the underlying ”base” network or the output of one of the base network’s intermediate layers whether the base network will be correct or incorrect for a particular input. We find that, over a wide range of tasks and base networks, the meta network can achieve accuracies ranging from 65% 85% in making this determination.
IDETC 2016-59038 UNDERSTANDING THE IMPACT OF EXPERTISE ON DESIGN OUTCOME : AN APPROACH BASED ON CONCEPT INVENTORIES AND ITEM RESPONSE THEORY

Research on expertise in design has focused primarily on understanding expert-novice differences. Although it is well established that experts perform better than novices, there is a lack of formal methods to quantify the potential impact of expertise on the quality of design outcomes. The research question addressed in this paper is: How can the impact of expertise on the quality of design solutions be quantified? Quantifying such impacts can be of particular importance in product development, recruitment processes and design competitions. We utilize an approach based on Item Response Theory (IRT) and Concept Inventories (CI) for expertise quantification. We then investigate and validate the impact of expertise on solution quality through a behavioral experiment involving a track design problem. The results highlight the usefulness of the proposed approach and provide a functional relationship between expertise and solution quality. We also observe behavioral differences between participants with varying scores on a test taken in the behavioral experiment. The proposed approach could be used in the future work to quantify learning.
Diplomarbeit Analysis of full-waveform airborne laser scanning data for the improvement of DTM generation

This thesis deals with the potential of full-waveform airborne laser scanning for improved digital terrain model generation. The additional observables amplitude, echo width and backscatter cross section from this new scanning technique are separately analysed to assess their discriminatory power for classifying airborne laser scanner point clouds into terrain and off-terrain points. Based on the results of the exploratory data analysis, three different approaches for the extraction of terrain points are presented and tested in a practical application. On the one hand, hard thresholds for the full-waveform observables are applied on the single points to discriminate into terrain and off-terrain. On the other hand, the full-waveform information is used to derive weights for the single points, describing the probability for belonging to the class "terrain". Proper probability and weight functions are introduced. The third method is segmentation-based. A seeded region growing algorithm is used to gather points with equal full-waveform attributes. A pragmatic concept for the extraction of segments representing terrain is described. Digital terrain models are computed from each of the three classified terrain point clouds. A comparison of these three models with a conventional terrain model, computed without the additional observables, points out the potential for improvement by integrating full-waveform measurements. In areas of dense vegetation with poor penetration rates of the laser measurement an enhancement in computing performance, as well as in adequacy of the resulting digital terrain models could be observed.
Ultrasonics , 2015 ICU Metz Dynamic homogenization of acoustic metamaterials with coupled field response

Acoustic metamaterials (AMM) are heterogeneous materials with dynamic subwavelength structures that can generate useful effective responses of interest to ultrasonic imaging applications such as negative refraction and zero index. Traditional effective medium models fail to capture details of frequency dependent AMM response and can give non-causal properties. This work derives non-local expressions for effective properties for an infinite periodic lattice of heterogeneities in an isotropic fluid using conservation of mass and momentum and the equation of state. The resulting model correctly predicts a causal effective material response by considering coupling between the ensemble-averaged volume strain and momentum fields. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of 2015 ICU Metz.
DNA Sequencing: Current State and Prospects of Development

In this review, we collected and classified the stages of development of DNA sequencing methods and described its peculiarities. We pay attention mostly on solid-stead nanopore sequencing methods. Detailed discussion of the peculiarity and feasibility of the electrical methods of DNA sequencing is discussed. The detail analyses of the literature data, some critical considerations and the potential ways of optimization of DNA nanopore sequencing were presented.
Emergence of Implicit Filter Sparsity in Convolutional Neural Networks

We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay. Through an extensive empirical study (Mehta et al., 2019) we hypothesize the mechanism behind the sparsification process. We find that the interplay of various phenomena influences the strength of L2 and weight decay regularizers, leading the supposedly non sparsity inducing regularizers to induce filter sparsity. In this workshop article we summarize some of our key findings and experiments, and present additional results on modern network architectures such as ResNet-50.
Identifying Interpretable Action Concepts in Deep Networks

A number of recent methods to understand neural networks have focused on quantifying the role of individual features. One such method, NetDissect identifies interpretable features of a model using the Broden dataset of visual semantic labels (colors, materials, textures, objects and scenes). Given the recent rise of a number of action recognition datasets, we propose extending the Broden dataset to include actions to better analyze learned action models. We describe the annotation process and results from interpreting action recognition models on the extended Broden dataset.
Towards Provably Correct Driver Assistance Systems through Stochastic Cognitive Modeling

The aim of this study is to introduce a formal framework for analysis and synthesis of driver assistance systems. It applies formal methods to the verification of a stochastic human driver model built using the cognitive architecture ACT-R, and then bootstraps safety in semi-autonomous vehicles through the design of provably correct Advanced Driver Assistance Systems. The main contributions include the integration of probabilistic ACT-R models in the formal analysis of semi-autonomous systems and an abstraction technique that enables a finite representation of a large dimensional, continuous system in the form of a Markov model. The effectiveness of the method is illustrated in several case studies under various conditions.
Comparative bacterial analysis of chronic and aggressive periodontitis in a sample population from Uruguay

OBJECTIVES: To compare the prevalence of certain periodontal pathogens in aggressive and chronic periodontitis in 101 Uruguayan patients according to the valid classification at the time the studies were conducted. METHODS: This analysis is based on studies conducted to detect periodontal pathogens in patients with chronic and aggressive periodontitis in Uruguay using conventional and molecular methods. Both studies analyzed the same microorganisms and used the same recovery and analysis techniques. Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Prevotella intermedia and Fusobacterium nucleatum were studied. RESULTS: A higher prevalence of F. nucleatum, T. forsythia and P. gingivalis was detected in chronic cases, whereas higher rates of F. nucleatum and P. intermedia were observed in aggressive ones. Within the last one, a higher proportion of P. gingivalis and T. forsythia was detected in the generalized cases and A. actinomycetemcomitans in the localized ones.
On the Robustness of Human Pose Estimation

This paper provides, to the best of our knowledge, the first comprehensive and exhaustive study of adversarial attacks on human pose estimation. Besides highlighting the important differences between well-studied classification and human pose-estimation systems w.r. t. adversarial attacks, we also provide deep insights into the design choices of pose-estimation systems to shape future work. We compare the robustness of several pose-estimation architectures trained on the standard datasets, MPII and COCO. In doing so, we also explore the problem of attacking non-classification based networks including regression based networks, which has been virtually unexplored in the past. We find that compared to classification and semantic segmentation, human pose estimation architectures are relatively robust to adversarial attacks with the single-step attacks being surprisingly ineffective. Our study show that the heatmap-based pose-estimation models fare better than their direct regression-based counterparts and that the systems which explicitly model anthropomorphic semantics of human body are significantly more robust. We find that the targeted attacks are more difficult to obtain than untargeted ones and some body-joints are easier to fool than the others. We present visualizations of universal perturbations to facilitate unprecedented insights into their workings on pose-estimation. Additionally, we show them to generalize well across different networks on both the datasets.
Competence-based Curriculum Development for Business Informatics

This paper presents the theoretical and methodological approach used in the development of a business informatics study programme, in the field of social science. The paper advances the theoretical perspectives of a competency-based curriculum, as steps taken toward innovative curriculum development efforts, with further focus on the developed theoretical framework used in the analysis and design of a competency-based curriculum for a business informatics study programme. This theoretical framework was based on several guides and case studies on competency-based programme planning and curriculum development. Identified digital competencies served as a basis for determining the content of the courses in business informatics study programme that would meet the primary objective and be relevant in the light of digital transformation of business.