Miracle-dz/UniFAR-FTUs huggingface dataset:
My music and I: Decoding Individual Differences via Musical Behaviour

Music is omnipresent and has existed since time immemorial. In general, individuals’ response to music, be it in terms of movements or listening strategies, tend to be dictated by features intrinsic to music such as rhythmic structure, genre, in addition to extrinsic factors such as mood states, socio-cultural norms, amongst others. However, at the same time, human differences in physical, psychological, and behavioural traits affect the decisions we make and the experiences we pursue in daily life. Individual differences can refer to gender, cognitive styles of thinking, personality, amongst others. In this thesis, we try to expand our understanding of how individual characteristics modulate our musical behaviour. The study and analyses presents various angles of approaching musical behaviour in an interdisciplinary manner, particularly in the areas of embodied music cognition and music consumption. We particularly investigate musical behaviour in two broader sub-domains: Active Music Engagement, which entails corporeal involvement and Passive Music Engagement, which involves listening to music. In the first part, we look at decoding individual differences via active music engagement. The paradoxical balance between universality and individuality in human motoric responsiveness to music makes it interesting to note that all the individuals’ traits are encoded in free dance movement. The study addresses this by identifying individual differences, specifically gender, cognitive styles, Big Five personality traits, and music preferences using free dance movements via our proposed Machine Learning model. We further demonstrate the robustness of the proposed model by testing its efficacy on another data set and further providing conclusive evidence about the learned models’ generalizability. The results of the study support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music. In the second study, which deals with passive engagement, we investigate out how individual differences, specifically personality, are associated with the kind of emotional experiences one seeks from lyrics. We chose to investigate lyrics specifically because they are under-explored in the field of music information retrieval despite them playing a crucial role in eliciting emotions. Firstly, we sought to identify emotional connotations of music based on lyrics following which we associate them with individual differences represented by personality traits. To this end, first we propose a novel deep learning architecture to identify emotional connotations of music based on lyrics, which we further compare with the existing deep learning and even traditional methods on relevant datasets and show state-of-the-art performance. Subsequently, we use this model to extract emotional preferences of individuals mined
MIT Open Access Articles Hierarchical Object Map Estimation for Efficient and Robust Navigation

—We propose a hierarchical representation of objects, where the representation of each object is allowed to change based on the quality of accumulated measurements. We initially estimate each object as a 2D bounding box or a 3D point, encoding only the geometric properties that can be well-constrained using limited viewpoints. With additional measurements, we allow each object to become a higher dimensional 3D volumetric model for improved reconstruction accuracy and collision-testing. Our Hierarchical Object Map Estimation (HOME) is robust to deﬁciencies in viewpoints and allows planning safe and efﬁcient trajectories around object obstacles using a monocular camera. We demonstrate the advantages of our approach on a real-world TUM dataset and during visual-inertial navigation of a quad-rotor in simulation.
Subfilter Scale Modelling for Large Eddy Simulation of Lean Hydrogen-enriched Turbulent Premixed Combustion

Hydrogen (H2) enrichment of hydrocarbon fuels in lean premixed systems is desirable since it can lead to a progressive reduction in greenhouse-gas emissions, while paving the way towards pure hydrogen combustion. In recent decades, large-eddy simulation (LES) has emerged as a promising tool to computationally describe and represent turbulent combustion processes. However, a considerable complication of LES for turbulent premixed combustion is that chemical reactions occur in a thin reacting layer at small scales which cannot be entirely resolved on computational grids and need to be modelled. In this thesis, subfilter-scale (SFS) modelling for LES of lean H2-enriched methane-air turbulent premixed combustion was investigated. Two- and three-dimensional fully-compressible LES solvers for a thermally perfect reactive mixture of gases were developed and systematically validated. Two modelling strategies for the chemistry-turbulence interaction were pursued: the artificially thickened flame model with a power-law SFS wrinkling approach and the presumed conditional moment (PCM) coupled with the flame prolongation of intrinsic low-dimensional manifold (FPI) chemistry tabulation technique. Freely propagating and Bunsen-type flames corresponding to stoichiometric and lean premixed mixtures were considered. Validation of the LES solvers was carried out by comparing predicted solutions with experimental data and other published numerical results. Head-to-head comparisons of different SFS approaches, including a transported flame surface density (FSD) model, allowed to identify weaknesses and strengths of the various models. Based on the predictive capabilities of the models examined, the PCM-FPI model was selected for the study of hydrogen-enrichment of methane. A new progress of reaction variable was proposed to account for NO. The importance of transporting species with different diffusion coefficients was demonstrated, in particular for H2. The proposed approach was applied to a Bunsentype configuration, reproducing key features observed in the experiments: the enriched flame was shorter, which is attributed to a faster consumption of the blended fuel; and the enriched flame displayed a broader two-dimensional curvature probability density function. Furthermore, reduced levels of carbon dioxide (CO2), increased levels of nitrogen monoxide (NO), and a slight increase in the carbon monoxide (CO) levels in areas of fully burned gas were predicted for the enriched flame.
The Association Between Physical Activity and Self-Rated Health Among Older Adults

Although there is substantial evidence on the association between physical activity and self-rated health among older adults globally, there is a paucity of findings on this topic for the aging Indonesian population. The present study was conducted to investigate the association between self-reported physical activity and self-rated health among older adults in Indonesia. The data used were from the fifth wave of the Indonesian Family Life Survey (IFLS). A multilevel regression approach was used for analysis. A sample of 1,813 older adults (aged 60 and over) within 311 communities was used in the study. Self-reported physical activity measured by time spent walking was the independent variable, and self-rated health was the dependent variable. The analysis was adjusted for gender, age, education level, marital status, religiosity, retirement status, neighborhood safety, extroversion, and subjective poverty. The results revealed that increased physical activity was consistently associated with better self-rated health in both unadjusted (β=0.04, p<0.05) and adjusted (β=0.06, p<0.01) models. To improve public health in Indonesia, primary health care professionals, health policymakers, and other health promoters should consider including physical activity in health policies and encourage older individuals to engage in regular physical activity.
On Modeling Glottal Source Information for Phonation Assessment in Parkinson's Disease

Parkinson’s disease produces several motor symptoms, including different speech impairments that are known as hypokinetic dysarthria. Symptoms associated to dysarthria affect different dimensions of speech such as phonation, articulation, prosody, and intelligibility. Studies in the literature have mainly focused on the analysis of articulation and prosody because they seem to be the most prominent symptoms associated to dysarthria severity. However, phonation impairments also play a signiﬁ-cant role to evaluate the global speech severity of Parkinson’s patients. This paper proposes an extensive comparison of different methods to automatically evaluate the severity of speciﬁc phonation impairments in Parkinson’s patients. The considered models include the computation of perturbation and glottal-based features, in addition to features extracted from a zero frequency ﬁltered signals. We consider as well end-to-end models based on 1D CNNs, which are trained to learn features from the raw speech waveform, reconstructed glottal signals, and zero-frequency ﬁltered signals. The results indicate that it is possible to automatically classify between speakers with low versus high phonation severity due to the presence of dysarthria and at the same time to evaluate the severity of the phonation impairments on a continuous scale, posed as a regression problem.
Oxidative Scrubber for NOx Emission Control Using NaClO2 Aqueous Solutions

Oxidative scrubbing using different oxidizing chemicals (e.g. sodium chlorite, NaClO2) has been proposed since several years for gas emissions control. Although higher performances than conventional after-treatment process currently in use can be reached, these technologies have not yet found a consolidated application in the power generation and other industrial fields, due to excessive chemical consumption and the formation of some harmful contaminants in the wash water that must be removed with specific treatment systems. This work reports an experimental investigation of an oxidative wet scrubber for NOx removal from a simulated flue-gas containing 1030 ppmv of nitrogen oxides, using a scrubbing liquid doped with sodium chlorite (NaClO2) as oxidizing chemical. The experiments are performed in a packed column operating in countercurrent flow with a constant gas velocity of 1.15 m/s at 60 °C and a liquid-to-gas ratio variable from 1.25 to 4.06 L/m. The packed column with a DN 100 is equipped with 892 mm of Mellapak 250.X packing. The experimental removal efficiencies of NOx are evaluated in function of the liquid flow rate and NaClO2 loading. These data allow to determine the specific NaClO2 dosage required to achieve a fixed NOx removal. Finally, a predictive correlation for wet oxidative scrubbing is also developed.
A qualitative analysis of vertical leadership development amongst NHS health-care workers in low to middle income country settings

Purpose The improving global health (IGH) programme is a leadership development programme that aims to develop leadership skills and behaviours alongside quality improvement methodology in National Health Service (NHS) employees in a global health setting. Through collaboration, experiential learning and mentorship, the programme aims to produce both vertical and horizontal leadership development in its participants. This paper aims to describe the programme and its impact, in terms of leadership development, in a sample of participants. Design/methodology/approach Open coding and thematic analysis of leadership development summaries (LDS) completed by 39 returned IGH participants were conducted. LDS are written on completion of the overseas placement; participants reflect on their personal leadership development against the nine dimensions of the NHS Healthcare Leadership Model (2013). Findings These IGH programme participants have reported a change in the way they think, behave and see the world. A development in sense of self and experience in developing team members are the two most commonly reported themes. Adaptability, communication, overcoming boundaries, collaborative working, “big picture” thinking and strategic thinking were also identified. Research limitations/implications The study is limited by the relatively low number of completed LDS. More work is needed to understand the long-term effect of this type of leadership development on the NHS. Other leadership development programmes should consider focussing on vertical and horizontal leadership development. Originality/value This more granular understanding of the leadership skills and behaviours developed and how it is the programme’s design that creates it, has not previously been described.
Historical Information for Enhancing Q-learning in Maze Navigation Tasks

When people are facing problems, they first search in their memories and try to find out useful information, or sometimes they ask help from others who might know the solutions. Here, people are using the knowledge they or others have learned before for solving problems. This historical knowledge could largely accelerate people’s solving process and enhance their performance when they are in any task. In this project, we are inspired by this idea and examined two different historical information under reinforcement learning(RL) settings and more precisely in maze navigation tasks using two RL methods Q-learning and its variant Deep Q-learning. The goal of the task is to control the agent in the maze to learn to find the optimal path from the start to the target. We believe historical information could also have positive impacts in our context as it does in real life. Historical information I is the record of visited cells, when we have this record, whenever the agent reaches cells which are in the record, a penalty will be assigned to the agent. Historical information II is the guide from the expert, the expert here knows the optimal path from the start to the target. These two different types of historical information are added to games and we experimentally proved that they help enhance these two RL methods to some extent and further make the agent reach the target way faster and stronger than the agent in normal games. Besides, in using historical information I, we studied how to set the optimal penalty which could make the agent the best performance. We trained a classifier that could predict the optimal penalty for unseen mazes and the accuracy could be up to 80%. Also, everything we developed is open-sourced for further study.
EsoCipher: An Instance of Hybrid Cryptography

This paper proposes a whole new concept in the field of Cryptography, i.e., EsoCiphers. Short for Esoteric Cipher, EsoCipher is an algorithm, which can be understood by a few, who have the knowledge about its backend architecture. The idea behind this concept is derived from esoteric programming languages, but EsoCiphers will be having a practical use in the future, as more research is done on the topic. Although the algorithm is quite simple to understand, the complexity of the output will indeed prove to be difficult to bruteforce if a secret is embedded to it. It uses a hybrid cryptography based technique, which combines ASCII, Binary, Octal and ROT 13 ciphers. The implementation and similarity index has been provided to show that it can be implemented practically.
A Momentum-Guided Frank-Wolfe Algorithm

With the well-documented popularity of Frank Wolfe (FW) algorithms in machine learning tasks, the present paper establishes links between FW subproblems and the notion of momentum emerging in accelerated gradient methods (AGMs). On the one hand, these links reveal why momentum is unlikely to be effective for FW-type algorithms on general problems. On the other hand, it is established that momentum accelerates FW on a class of signal processing and machine learning applications. Specifically, it is proved that a momentum variant of FW, here termed accelerated Frank Wolfe (AFW), converges with a faster rate <inline-formula><tex-math notation="LaTeX">${\cal O}(\frac{1}{k^2})$</tex-math></inline-formula> on such a family of problems, despite the same <inline-formula><tex-math notation="LaTeX">${\cal O}(\frac{1}{k})$</tex-math></inline-formula> rate of FW on general cases. Distinct from existing fast convergent FW variants, the faster rates here rely on parameter-free step sizes. Numerical experiments on benchmarked machine learning tasks corroborate the theoretical findings.
Optimal SSSC Design for Power Systems via Hybrid Approach

A novel hybrid approach involving Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization Algorithm (BFOA) called Bacterial Swarm Optimization (BSO) is illustrated for designing Static Synchronous Series Compensator (SSSC) for single and multimachine power systems. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual’s best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning PSSs compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power systems stability over a wide range of loading conditions. Key-Words: SSSC; Particle Swarm Optimization; Bacterial Foraging; Hybrid Algorithm; Damping Oscillations; Different Power Systems.
Effect of the Solvent on the Operating Cost of 5-hydroxymethylfurfural Downstream Processing

The bio-based circular economy raises demand for competitive processes to produce bio-based versatile chemicals. The 5-hydroxymethylfurfural is within this demand since it is a relevant building block not economically viable yet. A promising production process is based on a biphasic reactor where the HMF is rapidly extracted to the organic phase that is further evaporated until desirable product purity. The evaporation operating cost is the second leading contributor to the HMF final price, so the present study intended to investigate the solvent’s impact on energy demand and operating cost. The results showed that the solvent’s physical properties impact the cost, almost doubling the utility cost among alternatives. However, a careful observation also showed that the minimum production cost is way more affected by the reaction selectivity. Thus, it is worth it to increase the solvent flow (also increasing the energy demand and utility cost) as a strategy to increase the extraction capacity and, consequently, the reaction selectivity.
Capstone Project Report

This is the report of a capstone project carried out for the MSc in Data Science at Athens University of Economics and Business. The objective of this project was to design a recommendation engine which, given information about a candidate’s proﬁle, would be able to recommend relevant jobs. The task can be divided into two parts, an Information Retrieval (IR) part and a Machine Learning part. For the information retrieval part, Elastic Search engine was used in order to retrieve top 100 relevant jobs for a candidate. Then a deep neural network model called Deep Relevance Matching Model (DRMM) proposed by Guo et al was applied to rerank the results and retrieve the top 10 jobs for the candidate. The data used are given by the software recruitment company Workable and the evaluation of the results is done both automatically using typical IR measures and manually by examining the results one by one.
Optimization of Hybrid Distillation-hydrophilic and Organophilic Pervaporation Systems

The hybrid distillation-pervaporation process is an efficient method for the removal or recovery of volatile organic compounds from their aqueous mixtures. The hybrid distillation-pervaporation process has not only shown its excellent performance in the purification of solvent mixtures but also proven to have a potential of saving energy. In this study, the hybrid distillation-pervaporation process is optimized for the treatment of low-alcohol waste solvents. Three combinations are examined: distillation with hydrophilic pervaporation, distillation with organophilic pervaporation and distillation with both pervaporation methods. The hybrid systems result in a better performance in term of energy-use compared to conventional distillation. It is observed that a combination of distillation and hydrophilic or/and organophilic pervaporation is suitable for the separation of ethanol-water mixtures and the most recommended is the third layout.Keywords: hybrid distillation-pervaporation process, pervaporation, ethanol-water separation, azeo
Strategic Withholding and Imprecision in Asset Measurement: Online Appendix

This is the online appendix for Bertomeu et al.(2021). This document contains the remaining proofs of Section 3, as well as supplementary analyses when there are private costs of disclosure, when the firm can signal its type with a real action, when the firm uses its financial reports to meet a collateral constraint, and when precision is costly. The numbering of sections, equations, and results continues that of the appendix in the paper. C Proofs of Lemmas A.2-A.5 LemmaC.1 below demonstrates equilibrium existence in the disclosure subgame, for any given signal distributionG, and when there is no withholding penalty ( θ = 0). Lemma C.1 Fix the distribution of posterior means G and the withholding penalty θ = 0. Let X+ ≡ {x ∈ supp G : x ≥ ψ(x; G)} . (i) X+ is nonempty. (ii) x̂0 ≡ minX+ exists. ∗Jeremy Bertomeu and Edwige Cheynel are from the Olin School of Business at Washington University in St Louis. Davide Cianciaruso is from HEC Paris, 1 Rue de la Liberation, 78350 Jouy-en-Josas Cedex, France.
Adversarial Policy Learning in Two-player Competitive Games

In a two-player deep reinforcement learning task, recent work shows an attacker could learn an adversarial policy that triggers a target agent to perform poorly and even react in an undesired way. However, its efﬁcacy heavily relies upon the zero-sum assumption made in the two-player game. In this work, we propose a new adversarial learning algorithm. It addresses the problem by resetting the optimization goal in the learning process and designing a new surrogate optimization function. Our experiments show that our method signiﬁcantly improves adversarial agents’ exploitability compared with the state-of-art attack. Besides, we also discover that our method could augment an agent with the ability to abuse the target game’s unfairness. Finally, we show that agents adversarially re-trained against our adversarial agents could obtain stronger adversary-resistance.
A Setting for Rumor Containment Using Linear Threshold Models

: Rumor spreads fast in social networks and may produce signiﬁcant damages to the society. Blocking users in online social networks is normally used as a technical measure to control information spread. In this work, we provide a non-linear formulation to minimize rumor spread in linear threshold networks by blocking a subset of nodes in the network.
Process Superstructure Optimization for Resin and Aromatic Monomer Production from Kraft Lignin

Process Superstructure Optimization for Resin and Aromatic Monomer Production from Kraft Lignin Christos Galanopoulos, Aristide Giuliano*, Apostolis Koutinas, Isabella De Bari, Diego Barletta a Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, Athens, 11855, Greece b ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, S.S. 106 Ionica, km 419+500, Rotondella, MT, Italy c Dipartimento di Ingegneria Industriale, Universitá degli Studi di Salerno, Via Giovanni Paolo II 132, I-84084 Fisciano SA, Italy aristide.giuliano@enea.it