Miracle-dz/UniFAR-FTUs huggingface dataset:
A Meta-Path-Based Prediction Method for Disease Comorbidities

The simultaneous presence of diseases worsens the prognosis of patients and makes their treatment difficult. Identifying the co-occurrence of diseases is key to improving the situation of patients and designing effective therapeutic strategies. On the one hand, the increasing availability of clinical information opens new ways to unveil hidden relationships between diseases. On the other hand, heterogeneous information networks have been used in recent years to discover novel knowledge from disease data, including symptoms, genes or drugs. The use of meta-paths allows the complex semantics of the relationships between the different types of nodes to be included in heterogeneous networks. In this study, we propose a system to predict disease comorbidities through the use of meta-paths in a heterogeneous network of diseases and symptoms, built from textual sources of public access. The results obtained improve those of similar studies based on biological data, and the predictions calculated for diabetes and Crohn's disease are supported by medical literature. Both the used data and the obtained prediction model are publicly accessible.
Connective Fusion: Learning Transformational Joining of Sequences with Application to Melody Creation

We present Connective Fusion , a music generation scheme by transformational joining of two musical sequences for creative purposes. Given two shorter sequences as inputs, our model transforms each of them such that their concatenation is more coherent to form a longer sequence, while each of the transformed shorter sequences retains meaningful similarity with the corresponding input sequence. In short, our model connects and fuses two contextually unrelated sequences in a coherent way. This transformation can be applied iteratively to gradually fuse the input sequences. The style latent space is simultaneously learned, allowing users to control how the two sequences are merged. Our approach comprises two steps of unsupervised learning: a deep generative model with a latent space is learned, followed by adversarial learning of the transformation function in the latent space. We demonstrate the usefulness of our method through the task of melody creation using a symbolic music dataset.
The Rhythmic Dictator: Does Gamification of Rhythm Dictation Exercises Help?

We present the development and evaluation of a gami-ﬁed rhythmic dictation application for music theory learning. The application’s focus is on mobile accessibility and user experience, so it includes intuitive controls for input of rhythmic exercises, a responsive user interface, several gamiﬁcation elements and a ﬂexible exercise generator. We evaluated the rhythmic dictation application with conservatory-level music theory students through A/B testing, to assess their engagement and performance. The re-sults show a signiﬁcant impact of the application on the students’ exam scores.
A Neural Approach for Full-Page Optical Music Recognition of Mensural Documents

The digitization of the content within musical manuscripts allows the possibility of preserving, disseminating, and exploiting that cultural heritage. The automation of this process has been object of study for a long time in the ﬁeld of Optical Music Recognition (OMR), with a wide variety of proposed solutions. Currently, there is a tendency to use machine learning strategies based on neural networks because of their high performance and ﬂexibility to adapt to different scenarios by changing only the training data. However, most of the recent literature addresses only spe-ciﬁc parts of the traditional OMR workﬂow such as music object detection or symbol classiﬁcation. In this paper, we progress one step further by proposing a full-page OMR system for Mensural notation scores that consists of simply two processes, which are enough to extract the symbolic music information from a full page. More precisely, our pipeline uses Selectional Auto-Encoders to extract single staff regions, combined with end-to-end staff-level recognition based on Convolutional Recurrent Neural Networks for retrieving the music notation. The results conﬁrm the adequacy of our method, reporting a successful behavior on two Mensural collections (C APITAN and S EILS datasets) with a straightforward implementation.
Semantically Meaningful Attributes from Co-Listen Embeddings for Playlist Exploration and Expansion

Audio embeddings of musical similarity are often used for music recommendations and autoplay discovery. These embeddings are typically learned using co-listen data to train a deep neural network, to provide consistent triplet-loss distances. Instead of directly using these co-listen– based embeddings, we explore making recommendations based on a second, smaller embedding space of human-intelligible musical attributes. To do this, we use the co-listen–based audio embeddings as inputs to small attribute classiﬁers, trained on a small hand-labeled dataset. These classiﬁers map from the original embedding space to a new interpretable attribute coordinate system that provides a more useful distance measure for downstream applications. The attributes and attribute embeddings allow us to provide a search interface and more intelligible recommendations for music curators. We examine the relative performance of these two embedding spaces (the co-listen–audio embedding and the attribute embedding) for the mathematical separation of thematic playlists. We also report on the usefulness of recommendations from the attribute-embedding space to human curators for automatically extending thematic playlists.
Explaining Perceived Emotion Predictions in Music: An Attentive Approach

Dynamic prediction of perceived emotions of music is a challenging problem with interesting applications. Utilization of relevant context in audio sequence is essential for effective prediction. Existing methods have used LSTMs with modest success. In this work we describe three attentive LSTM based approaches for dynamic emotion prediction from music clips. We validate our models through extensive experimentation on standard dataset annotated with arousal-valence values in continuous time, and choose the best performer. We ﬁnd that the LSTM based attention models perform better than the state of the art transformers for the dynamic emotion prediction task, both in terms of R 2 and Kendall-τ metrics. We explore individual smaller feature sets in search of a more effective one and to understand how different features contribute to perceived emotion. The spectral features are found to perform at par with the generic ComPare feature set [1]. Through attention map analysis we visualize how attention is distributed over music clips’ frames for emotion prediction. It is observed that the models attend to frames which contribute to changes in reported arousal-valence values and chroma to produce better emotion predictions, effectively capturing long-term dependencies.
Evaluating a Longitudinal Synthetic Data Generator using Real World Data

Synthetic data offer a number of advantages over using ground truth data when working with private and personal information about individuals. Firstly, the risk of identifying individuals is reduced considerably, which enables the sharing of data for analysis amongst more organisations. Secondly, the fine tuning of synthetic datapoints to suit particular modelling and analyses could help to build more suitable models that can avoid biases found in the original ground truth data. In this paper we explore how a probabilistic synthetic data generator can be used to model data with high enough fidelity that it can be used to develop and validate state-of-the-art machine learning models. In particular, we use a Bayesian network model trained on gestational diabetes data, generated from a mobile health app collected from a number of health trusts in the UK. These data are used to train and test an established machine learning model developed by Sensyne Health using real-world data, and the resulting performance is compared to performance on ground truth data. In addition, a clinical validation is undertaken to explore if human experts can differentiate real patients from synthetic ones. We demonstrate that the Bayesian network synthetic data generator is able to mimic the ground truth closely enough to make it difficult for a human expert to distinguish between the two. We show that the data generator captures the interactions between features and the multivariate distributions close enough to enable classifiers to be inferred that imitate the key performance characteristics of models inferred from ground truth data. What is more, we demonstrate that the discovered mis-classifications found when testing using the synthetic data, are as informative as when testing using ground truth data.
Attributes-Aware Deep Music Transformation

Recent machine learning techniques have enabled a large variety of novel music generation processes. However, most approaches do not provide any form of interpretable control over musical attributes, such as pitch and rhythm. Obtaining control over the generation process is critically important for its use in real-life creative setups. Nevertheless, this problem remains arduous, as there are no known functions nor differentiable approximations to transform symbolic music with control of musical attributes.In this work, we propose a novel method that enables attributes-aware music transformation from any set of musical annotations, without requiring complicated derivative implementation. By relying on an adversarial confusion criterion on given musical annotations, we force the latent space of a generative model to abstract from these features. Then, reintroducing these features as conditioning to the generative function, we obtain a continuous control over them. To demonstrate our approach, we rely on sets of musical attributes computed by the jSymbolic library as annotations and conduct experiments that show that our method outperforms previous methods in control. Finally, comparing correlations between attributes and the transformed results show that our method can provide explicit control over any continuous or discrete annotation.
Modeling urban regions: Comparing random forest and support vector machines for cellular automata

Cellular automaton (CA) are important tools that provide insight into urbanization dynamics and possible future patterns. The calibration process is the core theme of these models. This study compares the performance of two common machine‐learning classifiers, random forest (RF), and support vector machines (SVM), to calibrate CA. It focuses on the sensitivity analysis of the sample size and the number of input variables for each classifier. We applied the models to the Wallonia region (Belgium) as a case study to demonstrate the performance of each classifier. The results highlight that RF produces a land‐use pattern that simulates the observed pattern more precisely than SVM especially with a low sample size, which is important for study areas with low levels of land‐use change. Although zoning information notably enhances the accuracy of SVM‐based probability maps, zoning marginally influences the RF‐derived probability maps. In the case of the SVM, the CA model did not significantly improve due to the increased sample size. The performance of the 5,000 sample size was observed to be better than the 15,000 sample size. The RF‐driven CA had the best performance with a high sample, while zoning information was excluded.
Easing the Questioning of Semantic Biomedical Data

Researchers have been using semantic technologies as essential tools to structure knowledge. This is particularly relevant in the biomedical domain, where large dataset are continuously generated. Semantic technologies offer the ability to describe data and to map and linking distributed repositories, creating a network where the searching interface is a single entry point. However, the increasing number of semantic data repositories that are publicly available is creating new challenges related to its exploration. Despite being human and machine-readable, these technologies are much more challenging for end-users. Querying services usually require mastering formal languages and that knowledge is beyond the typical user's expertise, being a critical issue in adopting semantic web information systems. In particular, the questioning of biomedical data presents specific challenges for which there are still no mature proposals for production environments. This paper presents a solution to query biomedical semantic databases using natural language. The system is at the intersection between semantic parsing and the use of templates. It makes it possible to extract information in a friendly way for users who are not experts in semantic queries.
Interfacial Tension of Methylbenzene–Water in the Presence of Hydrophilic and Hydrophobic Alumina Nanoparticles at Different Temperatures

The effect of almost low concentrations of alumina hydrophilic (bare surface) and hydrophobic (modified surface with hexadecanoic acid) nanoparticles on interfacial tension of methylbenzene (toluene)–water was investigated based on experimental results from drop weight tensiometry. Nanoparticles were suspended in either aqueous or organic phases to provide a stable nanofluid and then were contacted with another phase to measure the interfacial tension. A nanoparticle mass fraction range of 1·10–5 to 5·10–4 (± 5·10–6) and a temperature range of [293.2 to 323.2 (± 0.1)] K were used. Interesting trends of interfacial tension variation were revealed with nanoparticles concentration, showing a sharp increase up to 55.7 mN·m–1 (± 0.1 mN·m–1) at 293.2 K, and a decrease to about 14.0 mN·m–1 at 323.2 K with hydrophilic and hydrophobic forms, respectively. The effect of increasing temperature was found to reduce interfacial tension within the used ranges and significantly higher with hydrophilic nanoparticles.
Novel Method To Isolate Interfacial Material

Isolation of crude oil components that concentrate at the oil/water interface [i.e., interfacial material (IM)] facilitates their molecular identification and quantitation, which is critical for advances in optimal petroleum production and processing. For a given crude oil, the molecular composition of IM determines its emulsion stability and identifies those chemistries that disproportionately contribute to the interfacial layer. Here, we describe a scalable, simple, quick, and efficient procedure to isolate IM from petroleum crude oils and/or other organic matrices. Hydrated silica (∼26 monolayers of water on a silica surface) enables separation of interfacially active species through their interaction with the immobilized water. Species with little or no interaction with the hydrated silica (water-laden) surface are eluted with a 1:1 (vol) mixture of heptane/toluene (heptol). The interfacially active species are subsequently isolated through the addition of methanol to the eluting solvent mixture, whic.
N2O Pulse Titration of Ni/α-Al2O3 Catalysts: A New Technique Applicable to Nickel Surface-Area Determination of Nickel-Based Catalysts

Transmission electron microscopy (TEM), CO pulse, and N2O pulse titration were applied to measuring Ni particle sizes on Ni/α-Al2O3 catalysts. It was clarified for the first time that the N2O pulse titration could estimate the size at titration temperature between 50 and 100 °C, which was confirmed by TEM observation. This agreement in the Ni particle sizes by N2O titration and TEM observation means that the Ni surface was fully covered with the monolayer of oxygen formed by N2O decomposition and the adsorption of N2 and N2O on the catalysts was negligible. The Ni particle sizes estimated from the CO pulse technique were larger than those measured by the TEM observations. This is because the stoichiometry of CO to Ni was assumed to be one despite the complicated adsorption states of CO on Ni surface. On the contrary, the stoichiometry of oxygen to Ni using N2O pulse titration was one, which indicates that the stoichiometry measurements were not required for this method.
A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization

A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization Ahmet Dündar , Davut İzci , Serdar Ekinci , Erdal Eker 4,* 1 Vocational School of Social Sciences, Muş Alparslan University, Muş, https://orcid.org/0000-0002-2123-0564 2 Department of Electronics and Automation, Batman University, Batman, https://orcid.org/0000-0001-8359-0875 3 Department of Computer Engineering, Batman University, Batman, https://orcid.org/0000-0002-7673-2553 4 Accounting and Tax Department, Muş Alparslan University, Muş, https://orcid.org/0000-0002-5470-8384
Discourse-based Argument Segmentation and Annotation

The paper presents a discourse-based approach to the analysis of argumentative texts departing from the assumption that the coherence of a text should capture argumentation structure as well and, therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We tested the widely used Penn Discourse Tree Bank full parser (Lin et al., 2010) and the state-of-the-art neural network NeuralEDUSeg (Wang et al., 2018) and XLNet (Yang et al., 2019) models on the two-stage discourse segmentation and discourse relation recognition. The two-stage approach outperformed the PDTB parser by broad margin, i.e. the best achieved F1 scores of 21.2 % for PDTB parser vs 66.37% for NeuralEDUSeg and XLNet models. Neural network models were fine-tuned and evaluated on the argumentative corpus showing a promising accuracy of 60.22%. The complete argument structures were reconstructed for further argumentation mining tasks. The reference Dagstuhl argumentative corpus containing 2,222 elementary discourse unit pairs annotated with the top-level and fine-grained PDTB relations will be released to the research community.
EVALUATION OF INVITRO FREE RADICAL SCAVENGING POTENTIAL OF VARIOUS EXTRACTS OF WHOLE PLANT OF Mucuna pruriens (Linn)

Summary The antioxidant activity of various extracts of whole plant of Mucuna pruriens (Linn) was investigated in various in-vitro methods. The antioxidant activity was evaluated by Total antioxidant activity (Phosphomolybdic acid method), FRAP assay with reference standard Ascorbate and total flavonoids content respectively. The methanolic extract of Mucuna pruriens was found to more effective in the total antioxidant activity. The IC50 values of the methanolic extract of Mucuna pruriens and ascorbate were found to be 100µg/ml and 410µg/ml respectively. The methanolic extract of Mucuna pruriens was found more effective in FRAP assay than that of petroleum ether and ethyl acetate extracts. But when compare to the all the three extracts with ascorbate (standard), the methanolic extract of the Mucuna pruriens showed the better result. The methanolic extract of Mucuna pruriens contains high amount of flavonoids than that of other two extracts. Moreover, the results were observed in a concentration dependent manner. All the above in vitro studies clearly indicate that the methanolic extract of Mucuna pruriens has a better antioxidant activity. These in vitro assays indicate that this plant extracts is a better source of natural antioxidant, which might be helpful in preventing the progress of various oxidative stresses.
When life gives you oranges: detecting and diagnosing intermittent job failures at Mozilla

Continuous delivery of cloud systems requires constant running of jobs (build processes, tests, etc.). One issue that plagues this continuous integration (CI) process are intermittent failures - non-deterministic, false alarms that do not result from a bug in the software or job specification, but rather from issues in the underlying infrastructure. At Mozilla, such intermittent failures are called oranges as a reference to the color of the build status indicator. As such intermittent failures disrupt CI and lead to failures, they erode the developers' trust in the jobs. We present a novel approach that automatically classifies failing jobs to determine whether job execution failures arise from an actual software bug or were caused by flakiness in the job (e.g., test) or the underlying infrastructure. For this purpose, we train classification models using job telemetry data to diagnose failure patterns involving features such as runtime, cpu load, operating system version, or specific platform with high precision. In an evaluation on a set of Mozilla CI jobs, our approach achieves precision scores of 73%, on average, across all data sets with some test suites achieving precision scores good enough for fully automated classification (i.e., precision scores of up to 100%), and recall scores of 82% on average (up to 94%).
EFFICIENT DESIGN OF LOW LUNAR ORBITS BASED ON KAULA RECURSIONS

It is shown that the potential of the zonal problem of artificial satellite theory, up to an arbitrary degree, is more efficiently formulated using Kaula linear perturbation theory than other compact alternatives in the literature. Construction of the evolution equations based on this formulation is clearly more advantageous than the direct, brut force approach using symbolic algebra systems, and is applied to the design of low lunar orbits.
Electrochemical Behavior of Microbial Fuel Cell in a Start-up Phase

The electrochemical behavior of a typical H-type mediatorless microbial fuel cell(MFC), which was inoculated by the effluent of a primary sedimentation tank using potassium ferricyanide as catholyte,in the start-up phase was investigated.The results showed that the change of open circuit voltage proceeded the closed one in a start-up phase which revealed that the change of the internal resistance determined the start-up process.The reduction of potassium ferricyanide cathode potential was smaller than the anode potential,and the changes of MFC voltage was dominated by the anode.The analysis of electrochemical impedance spectroscopy(EIS)indicated that the charge transfer resistance reduced in the start-up phase,which reflected the reproduction and domestication of electricigens in MFC anode.
Design of miniaturized FSS using equivalent circuit model and multi-objective particle swarm optimization

A method for designing miniaturized frequency selective surface (FSS) using equivalent circuit model (ECM) and multi-objective particle swarm optimization (MOPSO) algorithm is proposed in this paper. In the proposed method, ECM describing the desired frequency response is established, and parameters of the ECM are determined using the MOPSO algorithm. Combining the ECM analysis and the MOPSO algorithm, the requirements of desired frequency response can be converted into the constraints of equivalent circuit parameters, which simplifies the optimization process of FSS design. For verification, a single-layer bandstop-bandpass FSS composed of convoluted elements with the unit cell size of 0. 073λ 0 × 0. 073λ 0 is designed. Simulation results show that the −15 dB bandwidth is from 2.62 to 2.83 GHz, and the −0.5 dB bandwidth is from 3.84 to 5 GHz. Meanwhile, the resonant frequency is stable at different incident angles (up to 80°) under TE and TM polarization. Finally, the proposed FSS prototype is fabricated and measured. The measured results agree well with the simulated ones.