Miracle-dz/UniFAR-FTUs huggingface dataset:
Using psychophysiological measures to recognize personal music emotional experience

Music can trigger human emotion. This is a psychophysiological process. Therefore, using psychophysiological characteristics could be a way to understand individual music emotional experience. In this study, we explore a new method of personal music emotion recognition based on human physiological characteristics. First, we build up a database of features based on emotions related to music and a database based on physiological signals derived from music listening including EDA, PPG, SKT, RSP, and PD variation information. Then linear regression, ridge regression, support vector machines with three different kernels, decision trees, k-nearest neighbors, multi-layer perceptron, and Nu support vector regression (NuSVR) are used to recognize music emotions via a data synthesis of music features and human physiological features. NuSVR outperforms the other methods. The correlation coefficient values are 0.7347 for arousal and 0.7902 for valence, while the mean squared errors are 0.023 23 for arousal and 0.014 85 for valence. Finally, we compare the different data sets and find that the data set with all the features (music features and all physiological features) has the best performance in modeling. The correlation coefficient values are 0.6499 for arousal and 0.7735 for valence, while the mean squared errors are 0.029 32 for arousal and 0.015 76 for valence. We provide an effective way to recognize personal music emotional experience, and the study can be applied to personalized music recommendation.
A deep convolutional feature based learning layer-specific edges method for segmenting OCT image

Optical coherence tomography (OCT) is a high resolution and non-invasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnostic. However, manual segmentation is often a time-consuming and subjective process. In this work, we present a new method for retinal layer segmentation in retinal optical coherence tomography images, which uses a deep convolutional feature to train a structured random forest classifier. The experimental results show that our method achieves good results with the mean distance error of 1.45 pixels whereas that of the state-of-the-art was 1.68 pixels, and achieve a F-score of 0.86 which is also better than 0.83 that is obtained by the state-of-the-art method.
A Web-based Nutritional Assessment Tool

The aim of this study was to present a web-based nutritional assessment tool that offers the possibility to construct the personal food pyramid and interprets the metabolic profile of a patient. A web application was developed both for healthcare professionals and the general public that besides analyzing the eating habits also provides some specific recommendations for every user. After filling in some general information the user has the choice either to go to the interpretation or to complete a food frequency questionnaire that allows a thorough analysis of his/hers eating pattern. The advantages of this tool are its accessibility, the construction of the personal food pyramid and the fact that is developed in a home country.
Parallel Path-based Static Analysis

Multicore architectures are widely used in research and industry today. The innovations in parallel computer architectures create an opportunity for the development of software solutions that exploit the available parallelism. Static program analysis is a well-explored area of research which has many applications including detecting software vulnerabilities, test case generation, debugging and program parallelization. For many types of static analysis, scalability is an important concern. In prior research, the performance of static analysis has been improved with techniques such as exploring a limited portion of the search space, flowinsensitivity, context-insensitivity, abstraction and the use of heuristics. Many of these techniques can be viewed as trade-offs that sacrifice precision in favor of scalability and practical applicability. Previous work has demonstrated that demand-driven query propagation is an effective and efficient static analysis technique that is more scalable than current techniques. In this research, we present a version of the demand-driven technique that is multithreaded to further increase scalability. The key idea that underlying our approach is that querybased demand-driven analysis exhibits significant opportunities for parallelization because queries typically have few dependencies. We present a parallel algorithm for statically detecting buffer overflows and evaluate its feasibility in a path-based static analysis framework called Marple. We implemented the scheme using Microsoft Phoenix [20] and Marple [15], a tool produced at University of Virginia.
Title Lead-DBS v 2 : Towards a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient‘s preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.
Masters Thesis: POMDP based online parameter estimation for autonomous passenger vehicles

The internal model is an important piece of the control system of an autonomous driving vehicle. In order for the model to deliver accurate predictions, a valid model structure and well chosen parameters are needed. Model parameters can be highly fluctuating or complex to predict, especially when looking into tyre ground surface interaction models. Instead of predicting parameter values beforehand, they could be estimated and updated in real-time. Fluctuation or incorrectness can be adjusted while driving. However, this uncertainty in parameter value must be accounted for when applying control. Solving this problem by regarding the uncertainty in parameters of the internal vehicle model as a POMDP has been researched in this paper. The research question being: is it worthwhile to use the POMDP approach for online parameter estimation of autonomous passenger vehicles? To answer this multiple sub-questions have been composed. We start off looking into: what is the most suitable vehicle model? Different vehicle models and tyre models were compared. Literature showed the bicycle model in combination with the linearized tyre model to be most suitable for autonomous passenger vehicles. The next question is: What is the most promising algorithm? Using literature, suitable algorithms for solving this POMDP have been found and compared. From three compelling algorithms, the one best fitting the autonomous driving criteria was chosen. Knowing the model and the algorithm for the simulation the next question became: Does the algorithm perform on a vehicle model? To answer this question, the simulation has been implemented in MATLAB and performance has been tested. The results showed significant increase in parameter estimation performance. Within 2 timesteps the estimate had converged correctly. The next question is: Does the algorithm perform within realistic bounds? To answer this question, the same simulation as before has been used, but now with saturation on the steering input. This showed parameter estimation performance increase compared to the original trajectory, but not as overwhelming as without saturation. The next question is: Does the algorithm suffer from high noise? To answer this question, the same simulation has been used, but now with different levels of noise. The results showed parameter estimation performance significantly affected by increasing noise. The final subquestion is: Does the algorithm suit increasing model complexity? To answer this question, the amount of parameters have been increased in the simulation and there has been looked into the large matrices that accompany the algorithm. Results showed that increasing the Master of Science Thesis Quinn Vroom 4225554
Industrial Management & Data Systems Emerald Article : An integrated framework for outsourcing risk management

Purpose – The purpose of this paper is to present a generic framework to assess and simulate outsourcing risks in the supply chain. Design/methodology/approach – This combination approach involves a qualitative risk analysis methodology termed as the supply chain risk-failure mode and effect analysis (SCR-FMEA) which integrates risk identification, analysis and mitigation actions together to evaluate supply chain outsourcing risk. The qualitative risk assessment will allow risk manager to provide a visual presentation of imminent risks using the risk map. Monte Carlo simulation (MCS) on the imminent risks of delivery outsourcing using the Milk-Run system is adopted. Findings – With basic statistical concepts, key performance variables and the risk of delivery outsourcing are analyzed. It is found that a newly implemented delivery outsourcing arrangement on the Milk-Run system reduces the average customer lead-time and total cost. However, a certain extent of risk or uncertainty can still be detected due to the presence of variation. Research limitations/implications – This paper reveals that company can manage the risk by adopting a systematic method for identifying the potential risks before outsourcing and MCS can be applied for examining the quantifiable risks such as lead time and cost. Practical implications – The paper provides a generic guideline for practitioners to assess logistics outsourcing, especially for logistics management consultants and professionals for evaluating the risk and impact of outsourcing. It is believed that the proposed risk assessment framework can help to analyze the operational cost uncertainty and ensure the stability of the supply chain. However, the limitation of this research is that the full spectrum of outsourcing risk, especially the non-quantifiable risk may not be analyzed by MCS. Originality/value – This paper proposed an integrated framework which combines qualitative and quantitative method together for managing outsourcing risk. This research provides a standardized metric to quantify risk in the supply chain so as to determine the effectiveness of outsourcing.
AUTOMATIC CLASSIFICATION OF MICROLENSING CANDIDATES A Thesis by Virisha Timmaraju Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE

ii DEDICATION.iii ACKNOWLEDGEMENTS. iv CONTRIBUTORS & FUNDING SOURCES. v NOMENCLATURE.vi TABLE OF CONTENTS. vii LIST OF FIGURES. x LIST OF TABLES. xii
OPTIMAL LINEAR QUADRATIC REGULATOR

Muhammad Zulizar Baihaqie, Aulia Siti Aisjah, Eko Budi Djatmiko Jurusan Teknik Fisika, Fakultas Teknologi Industri, Institut Teknologi Sepuluh Nopember (ITS) Surabaya Jurusan Teknik Kelautan, Fakultas Teknologi Kelautan, Institut Teknologi Sepuluh Nopember (ITS) Surabaya Jalan Arief Rahman Hakim Sukolilo – Surabaya 60111 Indoneisa Email : zulizar.baihaqie10@mhs.ep.its.ac.id , auliasa@ep.its.ac.id , ebdjatmiko@oe.its.ac.id 3
MODIFIKACE KONCEPTU SPOLEČENSKÉ ODPOVĚDNOSTI FIREM V KONTEXTU SPOLEČENSKÉ ODPOVĚDNOSTI UNIVERZIT

A potential opportunity for universities is application of the social responsibility concept. In the case of universities, the importance of this concept is, in view of their specific institutional character and mission, fundamental in comparison with the other types of economic entities. Therefore, it is necessary to develop the university social responsibility concept. And the basis can lie in the works from the area of corporate social responsibility. The article deals with the problems of modification of the corporate social responsibility concept in the context of the university social responsibility. It further develops the theoretical resources of corporate social responsibility and stakeholder theory. The basic resources of the university social responsibility are described and its key areas and activities are identified.
Lateralized Readiness Potentials Candidate : Supervisors : Remo Arena ( s 219565 )

The goal of this thesis is to investigate and improve the computation of the Lateralized Readiness Potential by using tools like Machine Learning and Dimensionality Reduction side by side with the traditional tools usually employed in neurophysiology, such as ltering and trials averaging. Within this work Lateralized Readiness Potentials (LRP) are investigated with the intention of understanding whether, in a near future, they could be used as a diagnostic tool in motor and consciousness disorders. A typical scenario in which could be employed is the diagnosis of a a LockedIn Syndrome. Locked-in syndrome (LIS ) is a condition in which a patient has a complete paralysis of all voluntary muscles except for vertical eye movements and blinking. Locked-in syndrome may be confused with a loss of consciousness in patients, thus misleading to a diagnosis of Vegetative State and it may even resemble death. Being the LRP, or more in general RP, associated to the intentionality of movement, it could unveil if there is still consciousness in what could be misunderstood as a muscle spasm. During this work of thesis the use of several methods and techniques were investigated. All these methods will be shown in the following, with particular emphasis on the ones employed in the software developed and on the reason why they were preferred to the others.
de Probabilistic Balance Monitoring for Bipedal Robots

In this paper, a probability-based balance monitoring concept for humanoid robots is proposed. Two algorithms are presented that allow us to distinguish between exceptional situations and normal operations. The first classification approach uses Gaussian-MixtureModels (GMM) to describe the distribution of the robot’s sensor data for typical situations such as stable walking or falling down. With the GMM it is possible to state the probability of the robot being in one of the known situations. The concept of the second algorithm is based on Hidden-Markov-Models (HMM). The objective is to detect and classify unstable situations by means of their typical sequences in the robot’s sensor data. When appropriate reflex motions are linked to the critical situations, the robot can prevent most falls or is at least able to execute a controlled falling motion. The proposed algorithms are verified by simulations and experiments with our bipedal robot
UvA-DARE ( Digital Academic Repository ) Primary hyperoxaluria type 1 : clinical , genetic and biochemical studies

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva. nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Integrated Architecture for Vision-based Indoor Localization and Mapping of a Quadrotor Micro-air Vehicle

Nowdays, the existing systems for autonomous quadrotor control are being developed in order to perform navigation in outdoor areas where the GPS signal can be used to define navigational waypoints and define flight modes like position and altitude hold, returning home, among others. However, the problem of autonomous navigation in closed areas, without using a global positioning system inside a room, remains a challenging problem with no closed solution. Most solutions are based on expensive sensors such as LIDAR or external positioning (f.e. Vicon, Optitrack) systems. Some of these solutions allow the capability of processing data from sensors and algorithms for external systems, which removes the intended fully autonomous component in a vehicle with such features. Thus, this project aims at preparing a small unmanned aircraft system, more specifically, a quadrotor, that integrates different frameworks which will allow simultaneous indoor localization and mapping where GPS signal is denied, using for such an RGB-D camera, in conjunction with other internal and external quadrotor sensors, integrated into a system that processes visionbased positioning and it is intended to carry out, in the near future, motion planning for navigation. The result of this project was an integrated architecture for testing localization, mapping and navigation modules, based on open-source and inexpensive hardware and available state-ofthe-art frameworks. It was also possible to partially test some localization frameworks, under certain test conditions and algorithm parameters. The mapping capability of the framework was also tested and approved. The obtained framework is navigation ready, needing only some adjustments and testing.
Evaluation of the shear bond and biaxial flexural strength of zircona core veneered by CAD / CAM and PRESSON technique

The aim of this in vitro study is to compare the shear bond and bi-axial flexural strengths of samples veneered with, CAD/CAM-fabricated zirconia core, and Press-on technique, with the definition its mode of failure. Twenty zirconia core discs were made using IPs e.max ZirCAD and CAD/ CAM technology. These were divided equally in to two groups. Group one was veneered by CAD-on technique using IPs e.max CAD, and group two was veneered by Press-on technique using IPs e.max ZirPress, then all the discs are thermocycled. The specimens were exposed to shear bond and biaxial flexural strength testing. Mode of failure was evaluated by SEM. All data were collected and analyzed statistically by SPSS, with significance level 5%, where P≤ 0.05. The ttest analysis of shear strength test showed statistically significant difference at (p < 0.05) for CAD-on group. While there were no statistically significant difference at (p < 0.05) for the biaxial flexural strength test between the two groups. CAD-on group showed adhesive/cohesive failure, while press-on group showed cohesive failure within the veneering ceramic. This study concluded that, the bond strength between zirconia core and veneer was affected by the veneering technique. CAD-on technique showed statistically significant high bond strength over Press-on technique
Interactive comment on “Using GRACE in a streamflow recession to determine drainable water storage in the Mississippi River Basin” by Heloisa Ehalt Macedo et al

General Comments: This manuscript describes a method of relating streamflow measurements and terrestrial water storage anomalies (TWSA) from GRACE data products to estimate the drainable storage of several Mississippi River sub basins. This research is current, relevant, and of interest to the readers of HESS. The manuscript was well written and organized, and I enjoyed reading it. I have a couple of concerns with the fundamental concepts that underpin this research that require further explanation from the authors, as described under ‘specific comments’ below. In addition, I have further
GENERATION AND FEEDBACK STRATEGIES

The airline industry is known as one of the most active industries on Twitter. Twitter fan pages are used by airlines for different purposes, e.g., customer service, complaint management, branding, and promotions. Firms try to motivate users to enhance fan page engagement. Thus, social computing techniques have become more important in recent years as these enable firms to improve their impact on fan page activities and to analyze user behavior. Apart from operational factors and reputation concerns, different fan page management strategies adopted by the airlines lead to different numbers of followers and followings, and, consequently, different rates of user engagement. This research explores how airlines develop their fan page strategies on Twitter, and how the similarity of generated content leads to higher engagement rates. We examine different aspects of firms’ tactics in reaction to users’ activities on fan pages, and common methods in managing content of fan pages. Our empirical study consists of a sample of 36 prominent airlines all around the world. We analyze the data obtained from a set of almost 153,992 postings (including tweets, retweets, and replies) that are published on the companies’ fan pages. Our results show that the topics of the company tweets can be categorized based on some characteristics of the fan page. Moreover, there is a relationship between a company’s activities on fan pages and number of followers.