Miracle-dz/UniFAR-FTUs huggingface dataset:
AIST Dance Video Database: Multi-Genre, Multi-Dancer, and Multi-Camera Database for Dance Information Processing

We describe the AIST Dance Video Database (AIST Dance DB) , a shared database containing original street dance videos with copyright-cleared dance music. Although dancing is highly related to dance music and dance information can be considered an important aspect of music information, research on dance information processing has not yet received much attention in the Music Information Retrieval (MIR) community. We therefore developed the AIST Dance DB as the ﬁrst large-scale shared database focusing on street dances to facilitate research on a variety of tasks related to dancing to music. It consists of 13,939 dance videos covering 10 major dance genres as well as 60 pieces of dance music composed for those genres. The videos were recorded by having 40 professional dancers (25 male and 15 female) dance to those pieces. We care-fully designed this database so that it can cover both solo dancing and group dancing as well as both basic choreography moves and advanced moves originally choreographed by each dancer. Moreover, we used multiple cameras surrounding a dancer to simultaneously shoot from various directions. The AIST Dance DB will foster new MIR tasks such as dance-motion genre classiﬁcation, dancer identi-ﬁcation, and dance-technique estimation. We propose a dance-motion genre-classiﬁcation task and developed four baseline methods of identifying dance genres of videos in this database. We evaluated these methods by extracting dancer body motions and training their classiﬁers on the basis of long short-term memory (LSTM) recurrent neural network models and support-vector machine (SVM) models.
Query by Video: Cross-modal Music Retrieval

Cross-modal retrieval learns the relationship between the two types of data in a common space so that an input from one modality can retrieve data from a different modality. We focus on modeling the relationship between two highly diverse data, music and real-world videos. We learn cross-modal embeddings using a two-stream network trained with music-video pairs. Each branch takes one modality as the input and it is constrained with emotion tags. Then the constraints allow the cross-modal embeddings to be learned with signiﬁcantly fewer music-video pairs. To retrieve music for an input video, the trained model ranks tracks in the music database by cross-modal distances to the query video. Quantitative evaluations show high accuracy of audio/video emotion tagging when evaluated on each branch independently and high performance for cross-modal music retrieval. We also present cross-modal music retrieval experiments on Spotify music us-ing user-generated videos from Instagram and Youtube as queries, and subjective evaluations show that the proposed model can retrieve relevant music. We present the music retrieval results at: http://www.ece. rochester. edu/~bli23/projects/query.html .
Mapping Timing Strategies in Drum Performance

How do drummers express different timing styles? We conducted an experiment in which we asked twenty-two professional drummers to perform a simple rhythmic pattern while listening to a metronome. Here, we investigate the strategies they employed to express three different instructed timing profiles for the same pattern: “On”, “Pushed” and Laid-back. Our analysis of the recordings follows three stages. First, we compute sixteen boolean features that capture the microtiming relations of the kick, snare and hi-hat drum onsets, between each other and with regards to the metrical grid. Second, we construct a microtiming profile (mtP) for every performance by averaging the boolean features across the recording. An mtP codifies the frequency with which the various features were found in a performance. Third, through a “similarity profiles” hierarchical clustering analysis, we identify groups of recordings with significant similarities in their mtPs. We found distinct strategies to express each intended timing profile that employ specific combinations of relations between the instruments and with regards to the meter. Finally, we created a map that summarizes the main characteristics of the strategies and their relations using a phylogenetic tree visualization.
Characterisation and application of tests for recent infection for HIV incidence surveillance

Three decades ago, the discovery of the Human Immunodeficiency Virus (HIV) was announced. The subsequent HIV pandemic has continued to devastate the global community, and many countries have set ambitious HIV reduction targets over the years. Reliable methods for measuring incidence, the rate of new infections, are essential for monitoring the virus, allocating resources, and assessing interventions. The estimation of incidence from single cross-sectional surveys using tests that distinguish between ‘recent’ and ‘non-recent’ infection has therefore attracted much interest. The approach provides a promising alternative to traditional estimation methods which often require more complex survey designs, rely on poorly known inputs, and are prone to bias. More specifically, the prevalence of HIV and ‘recent’ HIV infection, as measured in a survey, are used together with relevant test properties to infer incidence. However, there has been a lack of methodological consensus in the field, caused by limited applicability of proposed estimators, inconsistent test characterisation (or estimation of test properties) and uncertain test performance. This work aims to address these key obstacles. A general theoretical framework for incidence estimation is developed, relaxing unrealistic assumptions used in earlier estimators. Completely general definitions of the required test properties emerge from the analysis. The characterisation of tests is then explored: a new approach, that utilises specimens from subjects observed only once after infection, is demonstrated; and currently-used approaches, that require that subjects are followed-up over time after infection, are systematically benchmarked. The first independent and consistent characterisation of multiple candidate tests is presented, and was performed on behalf of the Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA), which was established to provide guidance and foster consensus in the field. Finally, the precision of the incidence estimator is presented as an appropriate metric for evaluating, optimising and comparing tests, and the framework serves to counter existing misconceptions about test performance. The contributions together provide sound theoretical and methodological foundations for the application, characterisation and optimisation of recent infection tests for HIV incidence surveillance, allowing the focus to now shift towards practical application.
Deep-EOS: General-Purpose Neural Networks for Sentence Boundary Detection

In this paper, we present three general-purpose neural network models for sentence boundary detection. We report on a series of experiments with long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) for sentence boundary detection. We show that these neural networks architectures outperform the popular framework of OpenNLP , which is based on a maximum entropy model. Hereby, we achieve state-of-the-art results both on multi-lingual benchmarks for 12 different languages and on a zero-shot scenario, thus concluding that our trained models can be used for building a robust, language-independent sentence boundary detection system.
A Data Compacting Technique to reduce the NetFlow size in Botnet Detection with BotCluster

Big data analytics helps us to find potentially valuable knowledge, but as the size of the dataset increases, the computing cost also grows exponentially. In our previous work, BotCluster, we had designed a pre-processing filtering pipeline, including whitelist filter and flow loss-response rate (FLR) filter, for data reduction, which intended to wipe out irrelative noises and reduce the computing overhead. However, we still face a data redundancy phenomenon in which some of the same feature vectors repeatedly emerged. In this paper, we propose a data compacting approach aimed to reduce the input volume and keep enough representative feature vectors to fit DBSCAN's (Density-based spatial clustering of applications with noise) criteria. It purges the redundant vectors according to a purging threshold and keeps the primary representatives. Experimental results have shown that the average data reduction ratio is about 81.34%, while the precision has only slightly decreased by 1.6% on average, and the results still have 99.88% of IPs overlapped with the previous system.
DeepSRGM - Sequence Classification and Ranking in Indian Classical Music Via Deep Learning

A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a melodic framework for compositions and improvisations alike. Raga Recognition is an important music information retrieval task in ICM as it can aid numerous downstream applications ranging from music recommendations to organizing huge music collections. In this work, we propose a deep learning based approach to Raga recognition. Our approach employs efficient pre possessing and learns temporal sequences in music data using Long Short Term Memory based Recurrent Neural Networks (LSTM-RNN). We train and test the network on smaller sequences sampled from the original audio while the final inference is performed on the audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during inference on the Comp Music Carnatic dataset and its 10 Raga subset respectively making it the state-of-the-art for the Raga recognition task. Our approach also enables sequence ranking which aids us in retrieving melodic patterns from a given music data base that are closely related to the presented query sequence.
A Schema-First Formalism for Labeled Property Graph Databases: Enabling Structured Data Loading and Analytics

Graph databases provide better support for highly interconnected datasets than relational databases. However, labeled property graph databases, which have become increasingly popular, are schema-optional, making them prone to data corruption, especially when new users switch from relational databases to graph databases. In this work, we provide a schema-driven formalism for graph databases. This formalism enables schema-driven loading of graph databases from other sources, such as relational databases. Also, this formalism enables schema-driven data analytics that allows for a more structured analysis of data stored in graph databases. Such analytics are based on a boilerplate approach allowing users who are not experts in the use of graph database query languages to carry out analytics efficiently. We showcase the utility of the proposed formalism by considering a case study from Airbnb for illustrating schema-based loading procedures. The proposed schema-driven analytics process is illustrated using another case study from an industrial cyber-physical systems standard. Overall, the schema-driven formalism provides several useful features, such as preventing both data corruption and long-term degradation of graph database structures.
FKIE - Offensive Language Detection on Twitter at GermEval 2019

We describe our submissions to the Shared Task on Identiﬁcation of Offensive Language at GermEval 2019. We take part in all three subtasks, utilizing a Support Vector Machine (SVM) for subtasks 1 and 2, and a Long short-term memory (LSTM) neural net as well as a Convolutional neural net (CNN) for subtask 3. We obtained a macro-F1 score of 75.21 for subtask 1, 55.42 for subtask 2 and 64.20 for subtask 3 on a development set that was split from the overall training set provided by the or-ganisers.
Towards Automatically Correcting Tapped Beat Annotations for Music Recordings

A common method to create beat annotations for music recordings is to let a human annotator tap along with them. However, this method is problematic due to the limited human ability to temporally align taps with audio cues for beats accurately. In order to create accurate beat annotations, it is therefore typically necessary to manually correct the recorded taps in a subsequent step, which is a cumbersome task. In this work we aim to automate this correction step by “snapping” the taps to close-by audio cues—a strategy that is often used by beat tracking algorithms to reﬁne their beat estimates. The main contributions of this paper can be summarized as follows. First, we formalize the automated correction procedure mathematically. Second, we introduce a novel visualization method that serves as a tool to analyze the results of the correction procedure for potential errors. Third, we present a new dataset consisting of beat annotations for 101 music recordings. Fourth, we use this dataset to perform a listening experiment as well as a quantitative study to show the effectiveness of our snap-ping procedure.
The HUIU Contribution to the GermEval 2019 Shared Task 1

In this paper, we present the HUIU system for the GermEval 2019 shared task 1. Our sys-tem uses linear SVMs with word and POS uni-grams and the number of authors as features. We obtain a micro-averaged F-score of 80.67 on the test data, thus ranking 15th out of 19 submissions, or 9th out of nine groups.
AkkuBohrHammer vs. AkkuBohrhammer: Experiments towards the Evaluation of Compound Splitting Tools for General Language and Specific Domains

We present a comparative evaluation study for splitting German compounds which belong to general language or to a speciﬁc domain. For the domain, we focus on DIY (”do-it-yourself”). The study consists of two parts: First, we evaluate three tools for compound splitting in German, one based on lexicons and corpus frequencies and two based on language-independent statistical processing. We introduce the tools, discuss the data and the construction of a gold standard, and show ﬁrst results for binary and ternary noun compounds, as well as for the handling of non-splittable items. In a second experiment, we post-train one of the splitters with text data from the DIY-domain, and evaluate the splitting performance on domain-speciﬁc compounds.
Abstract. MicroRNAs (miRNA/miRs) have been associated with the initiation and progression of non-small-cell lung cancer (NSCLC). Hence, a comprehensive understanding of the association between dysregulated miRNAs and NSCLC may contribute to the identification of novel therapeutic methods for patient

MicroRNAs (miRNA/miRs) have been associated with the initiation and progression of non-small-cell lung cancer (NSCLC). Hence, a comprehensive understanding of the association between dysregulated miRNAs and NSCLC may contribute to the identification of novel therapeutic methods for patients with NSCLC. MiRNA-433 (miR-433) has been reported to be dysregulated in numerous types of human cancers; however, its expression pattern, biological roles and associated mechanisms in NSCLC require further investigation. The present study aimed to detect miR-433 expression and determine its roles and underlying molecular mechanisms in NSCLC. In the present study, reverse transcription-quantitative polymerase chain reaction revealed that miR-433 was significantly downregulated in NSCLC tissues and cell lines. This decreased miR-433 expression was strongly associated with the tumor node metastasis stage and lymph node metastasis of patients with NSCLC. Cell Counting kit-8 and cell invasion assays revealed that the resumption of miR-433 expression decreased the proliferation and invasion of NSCLC cells. Bioinformatics analysis predicted E2F transcription factor 3 (E2F3) as a potential target of miR-433. Luciferase reporter assay, RT-qPCR and western blot analysis further demonstrated that E2F3 was a direct target of miR-433 in NSCLC. E2F3 downregulation induced by small interfering RNA exhibited inhibitory effects similar to those of miR-433 overexpression in NSCLC cells, and the restored E2F3 expression counteracted the suppressive effects on NSCLC cells induced by miR-433 overexpression. Therefore, miR-433 may inhibit the progression of NSCLC, at least in part, by targeting E2F3. The present study indicated that miR-433 may be investigated as an innovative candidate target for the therapy of patients with this fatal disease.
Self-efficacy and response-efficacy: critical components of sexual and reproductive health interventions targeting condom use intention among American Indian adolescents

ABSTRACT The Respecting the Circle of Life (RCL) intervention is a comprehensive, skills-based sexual/reproductive health program shown to be effective for reducing sexual risk among American Indian (AI) adolescents (13-19 years of age). This paper seeks to identify critical program components of the RCL intervention for replication of impacts on condom use intention (CUI) when scaling to additional communities. RCL was tested among AI adolescents through a cluster randomized controlled trial (N = 267) embedded in an 8-day basketball camp. Data were collected at baseline, immediately post-camp, at 6 and 12 months post-camp. Previously established predictors of CUI that were statistically significantly impacted by RCL at the post-camp time point were tested as mediators of RCL impact on CUI. Condom use self-efficacy and response efficacy fully mediated the effect of RCL on CUI. The indirect path through condom use self-efficacy had the greatest effect on CUI, explaining 79% of the direct effect. When stratified by gender, there was only evidence of mediation among girls. Results indicate condom use self-efficacy and response efficacy are critical components of the RCL intervention for AI girls, and sexual/reproductive health programs should include practical skills training to improve these constructs to maximize intervention impact on CUI.
Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization

Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolytic virotherapy would elicit an even greater immune response and improve patient outcomes. However, regimen scheduling of combination immunostimulation and T-VEC therapy has yet to be established. Here, we calibrate a computational biology model of sensitive and resistant tumour cells and immune interactions for implementation into an in silico clinical trial to test and individualize combination immuno- and virotherapy. By personalizing and optimizing combination oncolytic virotherapy and immunostimulatory therapy, we show improved simulated patient outcomes for individuals with late-stage melanoma. More crucially, through evaluation of individualized regimens, we identified determinants of combination GM-CSF and T-VEC therapy that can be translated into clinically-actionable dosing strategies without further personalization. Our results serve as a proof-of-concept for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.
Behavioral response of naïve and non-naïve deer to wolf urine

Large carnivores are recolonizing many regions in Europe, where their ungulate prey have lived without them for >150 years. Whether the returning large carnivores will modify ungulate behavior and indirectly affect lower trophic levels, depends on the ability of ungulates to recognize risk based on past encounters and cues indicating carnivore presence. In two case studies, we tested, by means of camera trapping, the behavioral response of deer to wolf urine. The first case study was in the Netherlands where deer (still) live in absence of wolves, and the second in Poland with long-term wolf presence. As controls we used water (no scent) and all-purpose soap (unfamiliar scent). Deer vigilance level on control plots was 20% in both case studies indicating that wolf occupancy per se does not lead to a consistent difference in behavior. Placing wolf urine did not significantly affect deer behavior in either the wolf-absent or the wolf-present area. More intense cues, or a combination of cues, are likely needed to affect deer behavior. Moreover, we found an unexpected reaction of deer towards all-purpose soap of reduced foraging (and tendency for increased vigilance) in the wolf-present area, whereas it did not affect deer behavior in the wolf-absent area. We hypothesize that deer associate all-purpose soap with human presence, causing no response in human-dominated landscapes (the Netherlands), but triggering a behavioral reaction in more remote areas (Poland). This illustrates attention should be paid to controls used in scent experiments as they may be associated differently than intended.
Combined Use of Deep Eutectic Solvents, Macroporous Resins, and Preparative Liquid Chromatography for the Isolation and Purification of Flavonoids and 20-Hydroxyecdysone from Chenopodium quinoa Willd

Deep eutectic solvents (DESs) were used in combination with macroporous resins to isolate and purify flavonoids and 20-hydroxyecdysone from Chenopodium quinoa Willd by preparative high-performance liquid chromatography (HPLC). The extraction performances of six DESs and the adsorption/desorption performances of five resins (AB-8, D101, HPD 400, HPD 600, and NKA-9) were investigated using the total flavonoid and 20-hydroxyecdysone extraction yields as the evaluation criteria, and the best-performing DES (choline chloride/urea, DES-6) and macroporous resin (D101) were further employed for phytochemical extraction and DES removal, respectively. The purified extract was subjected to preparative HPLC, and the five collected fractions were purified in a successive round of preparative HPLC to isolate three flavonoids and 20-hydroxyecdysone, which were identified by spectroscopic techniques. The use of a DES in this study significantly facilitated the preparative-scale isolation and purification of polar phytochemicals from complex plant systems.
miR-191 is involved in renal dysfunction in arsenic-exposed populations by regulating inflammatory response caused by arsenic from burning arsenic-contaminated coal

Chronic exposure to arsenic may result in the manifestation of damage in multiple organs or systems of the body. Arsenic-induced renal dysfunction has been determined, but their pathogenesis has not been fully examined. In this study, we measured the expression levels of miR-191 in plasma, the contents of pro-inflammatory (interleukin (IL)-6 and tumor necrosis factor alpha) and anti-inflammatory (IL-2 and transforming growth factor beta) cytokines, and renal dysfunction indicators (blood urea nitrogen, blood creatinine, uric acid, and cystatin C) in serum from control and arsenic poisoning populations and analyzed the relationship between the miR-191, cytokines, and renal dysfunction indicators. The results clearly show the alteration of miR-191 expression was significantly associated with arsenic-induced renal dysfunction. Overall, the association of miR-191, inflammatory response and renal dysfunction, is clearly supported by the current findings. In other words, miR-191 is involved in renal dysfunction in exposed populations by regulating inflammatory response caused by coal-burning arsenic. The study provides a scientific basis for further studies of the causes of the arsenic-induced renal dysfunction, the biological role of miR-191, and targeted prevention strategies.