Miracle-dz/UniFAR-FTUs huggingface dataset:
Underwater Piling Noise: Predicting sound levels in water with the Underwater Acoustic Simulator

This thesis investigates the important physical processes related to underwater sound propagation due to offshore pile driving with the first commercial and numerical model called the Underwater Acoustic Simulator (UAS). To make adequately use of noise mitigation measures during offshore wind projects and to protect marine life the prediction of sound levels in water is important. The aim of this thesis is to assess if the UAS is suitable to give insight in the physics contributing to sound propagation and if the UAS is suitable to quantify sound levels in water for the application in future wind park projects. This is done by comparing the predicted sound levels with the UAS with the measured sound levels obtained during the construction of the Gemini wind park. The results obtained with the UAS identify the parameters related to the seabed properties as the most important for governing the attenuation of the noise levels as function of distance and frequency. In the UAS the seabed is modelled as a fluid. Especially frequencies l 800 Hz are sensitive to the choice of geo-acoustic parameters. These frequencies show a trend of underestimation of the sound levels compared to the acoustic data. An increase of the predicted sound levels is observed for increasing the coarseness of the sediment of the upper seabed layer and decreasing this layer thickness. The UAS parameters can be tuned to obtain sound levels that agree with the measured sound levels in the Gemini area. However, these parameter values do not represent the properties of a realistic seabed. Additionally, no evident choice of values exist to arrive at sound levels that agree with the acoustic data. It is concluded that the choice of values compensates for the exclusion of the shear effect in the fluid approximation of the seabed in the UAS. The exclusion of the shear effect in the representation of the seabed in the UAS is regarded as essential. Tsouvalas (2015) shows that the propagation of sound energy at low frequencies is governed by the shear of the soil. These lower frequencies propagate in the deeper parts of the seabed where shear effects play a role. Also, sound energy in the form of Scholte waves propagates at the water-seabed interface, thereby contributing to the sound levels in water. The reason the sound levels propagating at low frequencies is sought in two aspects. In a fluid seabed the geo-acoustic parameters are adjusted and account for the damping associated with shear, while the propagation of sound energy at these wave forms is not modelled. Another possible explanation is the trapping of the low frequency pressure waves in the seabed. Although the UAS is able to predict sound levels in the Gemini area that agree well with the measured sound levels, this is not due to the correct implementation of the physic regarding the seabed. As a consequence, the model is tweaked to arrive at the measured. The UAS is therefore not considered suitable for application in future wind park projects.
Robust Controller Analysis and Design of Medical Haptic Control System

-Haptic interfaces for medial simulation prove to be especially useful for training in minimally invasive procedures. In this research, a framework that consists of relevant representation of operator and environment dynamic of a tele-manipulated haptic system is implemented. The uncertainty model of the operator and environment models is considered as well to study the influence on the aspects of stability and performance. The framework of H ∞ loop shaping robust controller design is applied, then the stability and performance analysis is investigated. Parametric robust control design method is used as well for comparison. The v-gap metric is considered to develop an accurate measurement of uncertainty. Based on the obtained value of the v-gap an efficient procedure of robust control design is applied. The obtained values of v-gap were 0.1112 for the master and 9.755*10 -4 for the slave parts of the haptic system, which indicate improved robustness of stability and performance as compared with the obtained values from parametric robust controller design.
Personality, family relationships and school climate dimensions as predictors of dispositional forgiveness in adolescence

The mental health benefits of forgiveness are well-established in the literature. The growing research on dispositional forgiveness in adolescence is indicative of the need to better understand its role in youth’s well-being. In order to examine dispositional forgiveness in adolescence, measures regarding Big 5 personality factors (individual level), family relationships, school climate (system level), and sociodemographic variables at an individual and system level were used. Participants were 1,378 adolescents attending junior and senior high schools located in the broader area of Attica, Greece, randomly selected. Younger age, lower levels of neuroticism, and higher levels of extraversion, agreeableness and quality communication with the mother predicted higher forgiveness of self. Higher levels of openness to experience, agreeableness, quality communication with the father, positive behavior, positive attitude, and lower levels of lack of respect in the school predicted higher forgiveness of others. Finally, lower levels of neuroticism, higher levels of extraversion, agreeableness, and positive behavior in the school predicted higher levels of forgiveness of situations. The findings highlight the important role of personality factors and indicate the significance of primary prevention interventions in the school community regarding school climate that could contribute to the promotion of dispositional forgiveness and well-being of adolescents.
Spirometric and anthropometric improvements in response to elexacaftor/tezacaftor/ivacaftor depending on age and lung disease severity

Introduction: Triple-combination cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy with elexacaftor/tezacaftor/ivacaftor (ETI) was introduced in August 2020 in Germany for people with CF (pwCF) ≥12 years (yrs.) of age and in June 2021 for pwCF ≥6 yrs of age. In this single-center study, we analyzed longitudinal data on the percent-predicted forced expiratory volume (ppFEV1) and body-mass-index (BMI) for 12 months (mo.) after initiation of ETI by linear mixed models and regression analyses to identify age- and severity-dependent determinants of response to ETI. Methods: We obtained data on 42 children ≥6–11 yrs, 41 adolescents ≥12–17 yrs, and 143 adults by spirometry and anthropometry prior to ETI, and 3 and 12 mo. after ETI initiation. Data were stratified by the age group and further sub-divided into age-specific ppFEV1 impairment. To achieve this, the age strata were divided into three groups, each according to their baseline ppFEV1: lowest 25%, middle 50%, and top 25% of ppFEV1. Results: Adolescents and children with more severe lung disease prior to ETI (within the lowest 25% of age-specific ppFEV1) showed higher improvements in lung function than adults in this severity group (+18.5 vs. +7.5; p = 0.002 after 3 mo. and +13.8 vs. +7.2; p = 0.012 after 12 mo. of ETI therapy for ≥12–17 years and +19.8 vs. +7.5; p = 0.007 after 3 mo. for children ≥6–11 yrs). In all age groups, participants with more severe lung disease showed higher BMI gains than those with medium or good lung function (within the middle 50% or top 25% of age-specific ppFEV1). Regression analyses identified age as a predictive factor for FEV1 increase at 3 mo. after ETI initiation, and age and ppFEV1 at ETI initiation as predictive factors for FEV1 increase 12 mo. after ETI initiation. Discussion: We report initial data, which suggest that clinical response toward ETI depends on age and lung disease severity prior to ETI initiation, which argue for early initiation of ETI.
Estimating Bias on Model Outputs Using Convolutional Neural Networks

Bias and uncertainty are key contributors of prediction errors in numerical weather and climate prediction. Biases can arise from various sources, including noise in the data, approximations in the observation operators used to simulate the data, or limitations of the assimilating algorithm. Although ensemble prediction systems can be computationally expensive as they require running multiple simulations in parallel, statistical post-processing techniques are often used to estimate bias and often used to inexpensively improve the prediction quality of the ensemble system. Many methodologies has been proposed in the literature to estimate bias and most of them are based on a mathematical formula for that quantification either processing the analysis increments, using the background residuals or by injecting the bias term in the cost function (Bias-Aware) techniques. On the other hand, the evolution of Machine Learning and more specifically Convolutional Neural Networks (CNNs) provide an intelligent way of detecting artifacts on images, classifying images, recognize patterns, perform texture segmentation etc. Certain work has been proposed using CNNs to detect bias/uncertainty but there is more that need to be done in the CNN architecture implementation to improve bias estimation in post processing. The approach of this work is focused on reviewing the literature on current methods for estimating bias in on Data Assimilation systems using conventional methods and Convolutional Neural Networks.
A Proof of Authority Blockchain Protocol for Secure Logging

—Due to the continuous interaction with internet services a person produces a high quantity of data every day. Some of this data is of a more private nature, nonetheless, we do not know who has access to it. In some cases, we do not even know of the existence of such sensitive data. Due to the current lack of transparency on the usage of online data and new regulations we propose a solution making use of Hyperledger Fabric, a permissioned blockchain with a Proof-of-Authority consensus algorithm, to create this new form of transparency for data logging while maintaining a secure and scaleable network. Such a network can be used internally at a company to abide by the new rules of the GDRP while facilitating the fair access to an employee’s data for both the employer and employee.
Android, Notify Me When It Is Time To Go Phishing

A mobile banking app just started up, and the notification "App updated, click here to restart" appears. The graphic theme is the same as the bank. Can we trust it? What if we cannot even trust that tapping an app actually loads the original one? More generally, what if Android notifies an attacker when her victim has just launched the target app of her phishing campaign so that she could cast the hook at the perfect moment? In this paper, we abuse inotify APIs, a mechanism for monitoring file system events, to mount a state inference-based phishing attack from a malicious app installed on the victim's smartphone. We also verified the novelty of our work analyzing 10,000 recent Android malware, and although we found some cases where malware uses inotify for their petty purposes, our attack seems to be publicly unknown.However, since Android constantly evolves year after year, we studied its feasibility over different Android versions and attacker's capabilities. By analyzing 4, 863 of the most popular apps, the most disconcerting finding is that if the attacker knows the installation path of the target app, all Android apps are vulnerable, regardless of the system version. Getting the installation path of an app is a capability that is only protected by a normal permission, and to make matters worse, there are workarounds to get it even without such permission.Even if this capability is denied, we propose different attack models under which this attack is still possible; however, at the end of our work, we provide the remediation to eradicate once and for all these attacks. Through this work, we reported three vulnerabilities to Google. Two were acknowledged as bugs of moderate severity, while the last one was already known but not public.
Trajectory-User Linking using Higher-order Mobility Flow Representations

Trajectory user linking (TUL) is a problem in trajectory classification that links anonymous trajectories to the users who generated them. TUL has various uses such as identity verification, personalized recommendation, epidemiological monitoring, and threat assessments. A major challenge in TUL modeling is sparse data. Previous TUL research heavily relies on sequence-to-sequence models such as RNNs and LSTMs, with trajectory segmentation to combat sparsity, but segmentation does not sufficiently address the issue and existing models often ignore data skewness, resulting in poor precision and performance. To address these problems, we present TULHOR, a TUL model inspired by BERT, a popular language representation model. One of TULHOR’s innovations is the use of higher-order mobility flow data representations enabled by geographic area tessellation. This allows the model to alleviate the sparsity problem and also to generalize better. TULHOR consists of a spatial embedding layer, a spatial-temporal embedding layer and an encoder layer, which encodes properties and learns a rich trajectory representation. It is trained in two steps, first using a masked language modeling task to learn general embeddings, then fine-tuned using a balanced cross-entropy loss to make predictions while handling imbalanced data. Experiments on real-life mobility data show TULHOR’s effectiveness as compared to current state-of-the-art models.
A Comparison of Energy Recovery by PATs against Direct Variable Speed Pumping in Water Distribution Networks

Water systems are usually considered low efficiency systems, due to the large amount of energy that is lost by water leakage and dissipated by pressure reducing valves to control the leakage itself. In water distribution networks, water is often pumped from the source to an elevated tank or reservoir and then supplied to the users. A large energy recovery can be realized by the installation of energy production devices (EPDs) to exploit the excess of pressure that would be dissipated by regulation valves. The feasibility of such a sustainable strategy depends on the potential of energy savings and the amount of energy embedded in water streams, assessed by means of efficiency measures. Alternatively, energy savings can be pursued if the water is directly pumped to the network, bypassing the elevated reservoir. This study focuses on the comparison of two solutions to supply a real network, assessed as a case study. The first solution consists of water pumping to a reservoir, located upstream of the network; the excess of energy is saved by the employment of a pump as turbine (PAT). The second scenario is characterized by a smaller pressure head since a direct variable speed pumping is performed, bypassing the reservoir. The comparison has been carried out in terms of required energy, assessed by means of a new energy index and two literature efficiency indices. Furthermore, differing design conditions have been analyzed by varying the pumping head of both the scenarios, corresponding to different distances and elevation of the water source.
Rate Against the Machine: Image Based Etsy Price Estimation

Estimating the art prices inside the Etsy art community is challenging due to both the subjective assessment and quantitative differences in the art. Real-world art has surface textures, geometry, lighting conditions, and shapes which combine to make the problem particularly difﬁcult. In this paper, we (1) introduce a model to process the Etsy website data, (2) combine this dataset with deep learning to build a large-scale price estimation system and (3) provide a web service to get access to it (etsyprice.com). Using many images scraped from Etsy, we train convolutional neural networks (CNNs) for two tasks: classifying images from Etsy, and beyond images, we provide a text encoder to use and reﬁne our model with every item’s description. For price estimation, we combine a CNN architecture and a Random Forest Regression achieving an 81.6% accuracy. After tuning and reﬁning our price estimation model, we used it to develop a web-service that enables anybody to get an estimation of her/his item before publishing an offer on Etsy, or simply to compare their art with others. This service provides links back to the most similar items in the scraped Etsy database to get an idea of the validity of the prediction.
Multi-focus image fusion based on block matching in 3D transform domain

Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However, most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image. This paper presents a fusion framework based on block-matching and 3D (BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low- and high- coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform (NSCT), non-subsampled Shearlet transform (NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.
On Understanding Disentanglement Puzzles

What makes a disentanglement puzzle a puzzle? In this paper, we explore this question from a geometric perspective. We study a set of wire puzzles—disentanglement puzzles consisting of two pieces formed by bending thick metal wire—and wire non-puzzles, contrast their geometric properties both in 3D space and in their 6D configuration space, and propose several qualitative and quantitative measures of how “puzzly” a wire puzzle is.
The HERD Project: Human-Multi-Robot Interaction in Search & Rescue and in Farming

—Large-scale multi-robot systems have numerous potential real-world applications. It is, however, still unclear how a human operator can effectively engage and control a system composed of multiple autonomous robots, especially in unstructured and outdoor environments. This paper reports on ongoing work in the project HERD — Human-AI Collaboration: Engaging and Controlling Swarms of Robots and Drones, in which we focus on two concrete use cases from industrial partners, namely farming and search & rescue. One of the industrial partners, Agro Intelligence ApS, currently sells autonomous farming robots, while the other, Robotto ApS, develops autonomous drone-based monitoring solutions for emergency responders. Both partners aim to scale their technologies to multi-robot/multi-drone operations. In this paper, we present the two use cases, their differences and similarities, challenges and preliminary results.
The Impact of Organizational Culture on Firm Performance: An Empirical Research on Indonesian Manufacturing Firms

Many papers have documented the impact of organizational culture on firm performance. Some authors conducted research on internally developed organizational culture, while other authors reported the impact of organizational culture that is influenced by its national culture. Indonesia has received much inflow of foreign direct investment in the past three years. Unfortunately, the impact of organizational culture stemmed from Indonesian national culture on firm performance received little attention. This study tries to confirm the impact of organizational culture on firm performance among Indonesia manufacturing firms. Data from 151 organizations are analyzed using structural equation modeling (SEM). The results confirm that organizational culture has significant effect in firm performance. Among the five culture dimensions, individualism has a stronger impact on firm performance, while uncertainty avoidance has only weak impact on firm performance.
Automatic acquisition of semantic classes for adjectives

This thesis concerns the automatic acquisition of semantic classes for adjectiv es. Our work builds on two hypotheses: first, that some aspects of the semantics of adjecti ves are not totally unpredictable, but correspond to a set of denotational types (seman tic classes). Therefore, adjectives can be grouped together according to their semantic class. Sec ond, that the semantic class of an adjective can be traced in more than one linguistic level. In partic ul , the morphology-semantics and syntax-semantics interfaces are explored for clues that lead to the acquisition of the targeted semantic classes. Since we could not rely on a previously established classification, a major ef fort is devoted to defining an adequate classification. The classification proposal is reach ed through an iterative methodology. By combining deductive and inductive approaches, we evo lve fr m an initial classification based on literature review to a final classification proposal th at takes advantage of the insight gained through a set of experiments. Each iteration consists of three steps: (a) a classification proposal is made ; (b) a number of classification experiments, involving human subjects and machine learning techniqu es, are carried out; (c) through the analysis of the experiments, advantages and drawba cks of the classification proposal are identified. We present a total of three iterations. The first one starts with a classificatio n based on literature review. A Gold Standard is built and tested against results obtained thr ough a series of unsupervised experiments. The analysis suggests a refinement of the cla ssification. The second iteration uses the refined classification and validates it through a new annota tio task performed by human subjects and a further set of unsupervised experiments. The third iteration incorporates three significant modifications: first, a large -scale human annotation task with 322 human subjects is carried out. For this task, the estimated agre ment ( K 0.31 to 0.45) is very low for academic standards. Thus, the achievement of reliable linguistic data is revealed as a major bottleneck. Second, the architecture used for th e automatic classification of adjectives is modified so that it allows for the acquisition of multiple class es, so as to account for polysemy. The best results obtained, 84% accuracy, impro ve upon the baseline by a raw 33%. Third, a systematic comparison between different levels of lingu istic description is performed to assess their role in the acquistion task at hand.