Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio Plugin Recommendation Systems for Music Production

Music actors operate several tools to achieve high-quality musical artifacts. One of the typically used tools is the digital multi-effects processor, which applies a chain of non-linear transformations (effects) to audio signals. However, the large number of available configurations implies that better use of such equipment is restricted to specialists. In this context, intelligent systems for music production aim to propose automatic tools to ease and support decision making by music actors. In this paper, we tackle the particular problem of selecting audio plugins, i.e., implementations of effects, by handling it as a recommendation task and following two distinct methodologies: supervised learning and collaborative filtering. To this, we introduce a formal probabilistic representation for the problem and evaluate the performance of standard machine learning techniques to obtain reliable recommendation systems using data collected from online repositories.
Scheduling Workloads of Workflows in Clusters and Clouds

This dissertation addresses three key challenges that are characteristic to the online scheduling of workloads of workflows in modern distributed computing systems. The first challenge is the realistic estimation of the resource demand of a workflow, as it is important for making good task placement and resource allocation decisions. Usually, workflows consist of segments with different parallelism and different interconnection types between tasks which affect the order how the tasks become eligible. Moreover, realistic task runtime estimates are not always available. The second challenge is the efficient placement of workflow tasks on computing resources for minimizing average workflow slowdown while achieving fairness. A wrongly chosen task placement policy can easily degrade the performance and negatively affect the fair access of workflows to computing resources. The third challenge is the automatic allocation (autoscaling) of computing resources for workflows while meeting deadline and budget constraints. Computing clouds make it possible to easily lease and release resources. Such decisions should be made wisely to minimize slowdowns and deadline violations, and to efficiently use the leased resources to reduce incurred costs. To address these challenges, this dissertation proposes novel scheduling policies for workloads of workflows and investigates the applicability of relevant state-of-the-art policies to the online scenario. For new policies, implementation effort and suitability for production systems are kept in mind. The considered workflow scheduling policies are experimentally evaluated by conducting a wide set of simulation-based and real-world experiments on a private multicluster computer. Additionally, a Mixed Integer Programming (MIP) approach is used to validate the obtained real-world experimental results versus the optimal solution from a MIP solver.
Particle Swarm Localization for Mobile Robots Using a 2D Laser Sensor

Mobile robot localization is a complex task, specially in unstructured indoor environments, due to noise and wrong data association from sensors. The localization procedure is even harder when the vehicle has low confidence about its last pose estimate, situation that requires a Global Localization procedure. In this work, a Global Localization algorithm based on Particle Swarm Optimization (PSO) is integrated with a Pose Tracking algorithm, the Perfect Match, to obtain a robust localization technique. Results show that this approach can solve the problem of Global Localization with good performance.
Music Emotion Recognition by Using Chroma Spectrogram and Deep Visual Features

Music has a great role and importance in human life since it has the ability to trigger or convey feelings. As recognizing music emotions is the subject of many studies conducted in many disciplines like science, psychology, musicology and art, it has attracted the attention of researchers as an up-to-date research topic in recent years. Many researchers extract acoustic features from music and investigate relations between emotional tags corresponding to these features. In recent studies, on the other hand, music types are classified emotionally by using deep learning through music spectrograms that involved both time and frequency domain information. In the present study, a new method is presented for music emotion recognition by employing pre-trained deep learning model with chroma spectrograms extracted from music recordings. The AlexNet architecture is used as the pre-trained network model. The conv5, Fc6, Fc7 and Fc8 layers of the AlexNet model are chosen as the feature extracting layer, and deep visual features are extracted from these layers. The extracted deep features are used to train and test the Support Vector Machines (SVM) and the Softmax classifiers. Besides, deep visual features are extracted from conv5_3, Fc6, Fc7 and Fc8 layers of the VGG-16 deep network model and the same experimental applications are made in order to find out the effective power of pre-trained deep networks in music emotion recognition. Several experiments are conducted on two datasets, and better results are obtained with the proposed method. The best result is obtained from the VGG-16 in the Fc7 layer as 89.2% on our dataset. According to the obtained results, it is observed that the presented method performs better.
A Siamese Neural Network Application for Sales Forecasting of New Fashion Products Using Heterogeneous Data

In the fashionmarket, the lack of historical sales data for new products imposes the use of methods based on Stock Keeping Unit (SKU) attributes. Recent works suggest the use of functional data analysis to assign the most accurate sales profiles to each item. An application of siamese neural networks is proposed to perform long-term sales forecasting for new products. A comparative study using benchmark models is conducted on data from a European fashion retailer. This shows that the proposed application can produce valuable item level sales forecasts.
Generic and specific computational principles for visual anticipation of motion trajectories. (Principes computationnels génériques et spécifiques à l'anticipation visuelle du mouvement)

La vision commence dans la retine, ou la lumiere est convertie en signaux electriques par les photorecepteurs. Les signaux sont envoyes aux cellules bipolaires puis aux cellules ganglionnaires, responsables de la formation des trains de potentiels d’action. L’information visuelle est ensuite transmise au thalamus par le nerf optique, qui la relaie au cortex visuel. La phototransduction seule necessite du temps, jusqu’a 150 ms, auxquelles s’ajoutent les delais introduits par les transmissions synaptiques entre les trois unites. Cela montre la necessite d’avoir des mecanismes compensatoires pour reduire les delais de traitement. Ces mecanismes sont connus sous le terme d’anticipation. L’anticipation se produit d’abord au niveau de la retine et se poursuit ensuite dans le cortex visuel primaire. Dans la retine, elle se caracterise soit par un decalage du pic de reponse des cellules ganglionnaires, soit par une onde d’activation a courte portee. Dans le cortex, elle se caracterise par une onde d’activation a plus grande portee. La premiere contribution de cette these est le developpement d’un modele d’anticipation dans la retine, avec trois types de cellules ganglionnaires : les cellules Fast OFF avec controle de gain, les cellules selectives a la direction avec connectivite via les synapses electriques et les cellules sensibles au mouvement differentiel couplees via les cellules amacrines. La deuxieme contribution consiste a utiliser notre modele comme entree d’un modele cortical capable de reproduire l’anticipation telle qu’observee dans d’imagerie optique. Nous avons etudie en particulier les phenomenes non lineaires impliques dans l’anticipation, ainsi que la connectivite, tant au niveau de la retine que du cortex visuel primaire. Le modele integre retine-cortex nous a permis d’etudier les effets de l’anticipation sur des stimuli en deux dimensions, et mettre en avant l’aspect collaboratif des mecanismes d’anticipation dans la retine et dans le cortex.
On Combining Diverse Models for Lyrics-Based Music Genre Classification

Automatic music organization and retrieval is a highly required task nowadays. Labeling songs with summarized but descriptive information have implications in a wide range of tasks in this scenario. The genre is one of the most common labels used for music recordings. Using this piece of information, music platforms can organize collections by, for instance, associating songs and artists with similar characteristics. Lyrics represent an alternative source of data for genre recognition. While "traditional" bag-of-words-based text mining techniques represent a considerable part of the literature, recent papers shown an advance on this task applying deep learning algorithms. However, there is no research on how these distinct strategies contribute to each other. In this paper, we explore different strategies for music genre classification from lyrics and show that even simple combinations of these strategies allow improving accuracy on the lyrics-based music genre identification.
Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.
Emotion-Aware and Human-Like Autonomous Agents

In human-computer interaction (HCI), one of the technological goals is to build humanlike artificial agents that can think, decide and behave like humans during the interaction. A prime example is a dialogue system, where the agent should converse fluently and coherently with a user and connect with them emotionally. Humanness and emotion-awareness of interactive artificial agents have been shown to improve user experience and help attain application-specific goals more quickly [45]. However, achieving human-likeness in HCI systems is contingent on addressing several philosophical and scientific challenges. In this thesis, I address two such challenges: replicating the human ability to 1) correctly perceive and adopt emotions, and 2) communicate effectively through language. Several research studies in neuroscience, economics, psychology and sociology show that both language and emotional reasoning are essential to the human cognitive deliberation process [41, 89, 90]. These studies establish that any human-like AI should necessarily be equipped with adequate emotional and linguistic cognizance. To this end, I explore the following research directions. • I study how agents can reason emotionally in various human-interactive settings for decision-making. I use Bayesian Affect Control Theory [81], a probabilistic model of human-human affective interactions, to build a decision-theoretic reasoning algorithm about affect. This approach is validated on several applications: two-person social dilemma games, an assistive healthcare device, and robot navigation. • I develop several techniques to understand and generate emotions/affect in language. The proposed methods include affect-based feature augmentation of neural conversational models, training regularization using affective objectives, and affectively diverse sequential inference. • I devise an active learning technique that elicits user feedback during a conversation. This enables the agent to learn in real time, and to produce natural and coherent language during the interaction. • I explore incremental domain adaptation in language classification and generation models. The proposed method seeks to replicate the human ability to continually learn from new environments without forgetting old experiences.
An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency.
Nonparametric clustering for image segmentation

Image segmentation aims at identifying regions of interest within an image by grouping pixels according to their properties. This task resembles the statistical one of clustering, yet many standard clustering methods fail to meet the basic requirements of image segmentation since the identified segments are often biased toward predetermined shapes and their number is rarely determined automatically. Nonparametric clustering is, in principle, free from these limitations and particularly suitable for the task of image segmentation. We discuss the application of nonparametric clustering to image segmentation and provide an algorithm specific for this task. Pixel similarity is evaluated in terms of the density of the color representation. The adjacency structure of the pixels is exploited to introduce a simple, yet effective method to identify image segments as disconnected high‐density regions. The proposed method answers to the need of both segmenting an image and detecting its boundaries and can be seen as a generalization to color images of the class of thresholding methods.
Dye-based chromoendoscopy following polypectomy reduces incomplete polyp resection

Abstract Background and study aims The completeness of a polyp resection is an important determinant of quality in colonoscopy, and may reduce incidence of interval cancers. Incomplete resection rates (IRR) vary widely and range from 6.5 % to 22.7 %. Residual disease is more likely with larger polyps, for sessile serrated adenomas, and with more proximal lesions. Chromoendoscopy is increasingly employed in lesion detection. The aims of this study were to assess local IRR, and to determine whether chromoendoscopy could correctly identify residual disease post polypectomy. Patients and methods This was a prospective study examining post polypectomy sites. Chromoendoscopy (0.13 % indigo carmine) was applied to resection bases to identify residual disease. Targeted base biopsies were taken from identified residual disease (positive group) or random base biopsies were taken when a clear base was visualised (negative group). Overall rates of incomplete resection were documented. Reported rates post chromoendoscopy and actual histological rates were documented and compared. Results A total of 102 polyps were identified for inclusion, of which 15 % (n = 16) were excluded. Resection quality was evaluated in 86 polyps of 61 patients (female n = 33 54 %; mean Age 62.3 years). Polyps were mainly removed by cold snare (n = 71, 82.5 %). Most polyps (n = 58, 67 %) measured between 5 to 10 mm. Polyps were largely located in the right colon (n = 57, 66 %). Overall histological residual disease occurred in 17 /86 (19.6 %). Chromoendoscopy correctly identified residual disease in 13 of 17 bases (76.5 %). Only four of /86 (4.6 %) of polyp bases were missclassified post-chromoendoscopy (odds ratio 0.284 (95 % CI 0.0857–0.9409), P = 0.03). Conclusion Indigo carmine chromoendoscopy improves early detection of residual disease post polypectomy, reducing incomplete resection rates.
Crystal and molecular structures of a binuclear mixed ligand complex of silver(I) with thiocyanate and 1H-1,2,4-triazole-5(4H)-thione

The centrosymmetric binuclear complex features a tetrahedral AgI centre within a S4 donor set. The three-dimensional molecular packing is sustained by amine-N—H⋯S(thione), N—H⋯N(triazolyl) and N—H⋯N(thiocyanate) hydrogen bonds.
Olindias deigo sp. nov., a new species (Hydrozoa, Trachylinae, Limnomedusae) from the Ryukyu Archipelago, southern Japan

Abstract A new hydromedusa belonging to the order Limnomedusae is reported from the Ryukyu Archipelago, southern Japan. Olindias deigosp. nov. can be distinguished from other Olindiidae species by the number and color of tentacles. Mature medusae of O. deigosp. nov. were collected to observe the life history, including polyp (hydroid) and medusa formation. A comparative table of the primary diagnostic characters of the genus is provided.
The Effectiveness of a New Topical Formulation Containing GSH-C4 and Hyaluronic Acid in Seborrheic Dermatitis: Preliminary Results of an Exploratory Pilot Study

Introduction Seborrheic dermatitis is a common skin disease with clinical aspects similar to those of psoriasis, eczema or allergic reactions, appearing on the sebum-rich areas of the scalp, face, and trunk. Yeast like Malassezia species, immunologic abnormalities and activation of complement are recognized as a crucial pathogen for the onset of seborrheic dermatitis. Intermittent and active phases are characterized by burning, scaling and itching, then followed by inactive periods. The disease is sometimes severe up to the erythrodermia; thus, it has a great influence on the patient’s quality of life. In vitro and vivo studies have shown that the exogenous intake of glutathione-GSH-C4 and tocopherol inhibits lipid peroxidation and effectively fights and reduces oxidative stress in inflammatory disorders. Methods We have carried out a study enrolling 20 patients affected by SD to evaluate the effectiveness and tolerability of a new topical formulation in cream (hereinafter SEB) containing GSH-C4 0.4% in hyaluronic acid 0.25% – a new synthetic glutathione derivate called INCI (butyroyl glutathione)-assigned by the Personal Care Council. Investigator Global Assessment score and Patient Global Assessment of Treatment scales were used to test the efficacy of this new formulation. Results All patients showed a good clinical response to the treatment with topical SEB demonstrated by the gradual reduction in inflammatory skin lesions. Discussion The results of our pilot study confirm the efficacy and tolerability of this new topical formulation in a real-life assessment and patients showed strong adherence to therapy. These promising results – still to be confirmed on a larger number of patients – emphasize the potential SEB has in controlling the chronic inflammation of seborrheic dermatitis.
Super-Enhancer-Associated Hub Genes In Chronic Myeloid Leukemia Identified Using Weighted Gene Co-Expression Network Analysis

Purpose Super-enhancer (SE)-associated oncogenes extensively potentiate the uncontrolled proliferation capacity of cancer cells. In this study, we aimed to identify the SE-associated hub genes associated with the clinical characteristics of chronic myeloid leukemia (CML). Methods Eigengenes from CML clinical modules were determined using weighted gene co-expression network analysis (WGCNA). Overlapping genes between eigengenes and SE-associated genes were used to construct protein–protein interaction (PPI) networks and annotate for pathway enrichment analysis. Expression patterns of the top-ranked SE-associated hub genes were further determined in CML patients and healthy controls via real-time PCR. After treatment of K562 cells with the BRD4 inhibitor, JQ1, for 24 hrs, mRNA and protein levels of SE-associated hub genes were evaluated using real-time PCR and Western blotting, respectively. H3K27ac, H3K4me1 and BRD4 ChIP-seq signal peaks were used to predict and identify SEs visualized by the Integrative Genomics Viewer. Results The yellow module was significantly related to the status and pathological phase of CML. SE-associated hub candidate genes were mainly enriched in the cell cycle pathway. Based on the PPI networks of hub genes and the top rank of degree, five SE-associated genes were identified: specifically, BUB1, CENPO, KIF2C, ORC1, and RRM2. Elevated expression of these five genes was not only related to CML status and phase but also positively regulated by SE and suppressed by the BRD4 inhibitor, JQ1, in K562 cells. Strong signal peaks of H3K27ac, H3K4me1 and BRD4 ChIP-seq of the five genes were additionally observed close to the predicted SE regions. Conclusion This is the first study to characterize SE-associated genes linked to clinical characteristics of CML via weighted gene co-expression network analysis. Our results support a novel mechanism involving aberrant expression of hub SE-associated genes in CML patients and K562 cells, and these genes will be potential new therapeutic targets for human leukemia.
A Critical Role of PCSK9 in Mediating IL-17-Producing T Cell Responses in Hyperlipidemia

We previously demonstrated that atherogenic Ldlr−/−Apobec1−/− (LDb) double knockout mice lacking both low-density lipoprotein receptor (LDLR) and apolipoprotein B mRNA-editing catalytic polypeptide-1 (Apobec1) had increased serum IL-17 levels, with T cell programming shifted towards Th17 cells. In this study, we assessed the role of proprotein convertase subtilisin/kexin type 9 (PCSK9) in T cell programming and atherogenesis. We deleted the Pcsk9 gene from LDb mice to generate Ldlr−/−Apobec1−/−Pcsk9−/− (LTp) triple knockout mice. Atherosclerosis in the aortic sinus and aorta were quantitated. Lymphoid cells were analyzed by flow cytometry, ELISA and real-time PCR. Despite of dyslipidemia, LTp mice developed barely detectable atherosclerotic lesions. The IL-17, was very low in plasma and barely detectable in the aortic sinus in the LTp mice. In the spleen, the number of CD4+CD8− cells and splenocytes were much lower in the LDb mice than LTp mice, whereas, the IL-17-producing cells of γδTCR+ T cells and effector memory CD4+ T cells (CD44hiCD4+) in the spleen were significantly higher in the LDb mice than in the LTp mice. The Rorc mRNA expression levels were elevated in LDb mice compared to LTp mice. When re-stimulated with an anti-CD3 Ab, CD44hiCD4+ T cells from LDb mice secreted more IL-17 than those from LTp mice. T cells from LDb mice (with PCSK9) produce more IL-17 at basal and stimulated conditions when compared with LTp mice (without PCSK9). Despite the dyslipidemic profile and the lack of LDLR, atherogenesis is markedly reduced in LTp mice. These results suggest that PCSK9 is associated with changes in T cell programming that contributes to the development of atherosclerosis.