Miracle-dz/UniFAR-FTUs huggingface dataset:
The Harmonix Set: Beats, Downbeats, and Functional Segment Annotations of Western Popular Music

We introduce the Harmonix set: a collection of annotations of beats, downbeats, and functional segmentation for over 900 full tracks that covers a wide range of western popular music. Given the variety of annotated music information types in this set, and how strongly these three types of data are typically intertwined, we seek to foster research that focuses on multiple retrieval tasks at once. The dataset includes additional metadata such as MusicBrainz identifiers to support the linking of the dataset to third-party information or audio data when available. We describe the methodology employed in acquiring this set, including the annotation process and song selection. In addition, an initial data exploration of the annotations and actual dataset content is conducted. Finally, we provide a series of baselines of the Harmonix set with reference beat-trackers, downbeat estimation, and structural segmentation algorithms.
A Supervised Learning Approach for the Extraction of Sources and Targets from German Text

We present the ﬁrst systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out speciﬁc differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classiﬁer is trained and tested on the dataset of the STEPS shared task.
Designing an Intelligent Platform for Drugs Administration

Health is essential and can be ensured only with proper nutrition, sports, and regular visits to the physician. The correct administration of drugs has an important role in the process of healing and cannot be treated superficially. Most often, people do not read the instructions from the leaflets because these are difficult to understand or contain too much information. This paper is centered on showcasing the user interface of an intelligent platform that combines patients’ clinical health data with information from drug leaflets in order to facilitate the administration of drug manufactured in Romania. First, clinical health data is extracted from wearable IoT devices and aggregated to create the patient’s profile. Second, the platform aggregates leaflets from various online sources and full-text search functionalities are obtained using Elasticsearch combined with a knowledge base. Third, patients are capable to undergo extensive search through the drug database, create their own calendar, receive reminders or alerts when certain drugs contain interacting substances. The platform’s goal is to increase the patients’ awareness over their health data and improve their lifestyle. This is the first application of its kind available for Romanian language. Author
Convolutional Neural Networks for Classification of German Blurbs

This paper presents the submission of the system AVERBIS BOHB CNN for the Shared Task on hierarchical classiﬁcation of German blurbs (short texts) - GermEval 2019 Task 1. We optimized the hyperparameters of a CNN based on a fastText word embedding layer and combined it with a variant of a T-Criterion classiﬁcation method. The model was able to achieve an F1 score of 0.834 (ranked 6th) for subtask a and of 0.644 (ranked 3th) for subtask b on the respective test sets.
Overview of GermEval Task 2, 2019 Shared Task on the Identification of Offensive Language

We present the second edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. Two subtasks were continued from the first edition, namely a coarse-grained binary classification task and a fine-grained multi-class classification task. As a novel subtask, we introduce the classification of offensive tweets as explicit or implicit. The shared task had 13 participating groups submitting 28 runs for the coarse-grained task, another 28 runs for the fine-grained task, and 17 runs for the implicit-explicit task. We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at https://projects.fzai. h-da.de/iggsa/
COMTRAVO-DS team at GermEval 2019 Task 1 on Hierarchical Classification of Blurbs

We present two systems developed by the Comtravo Data Science team for the Ger-mEval’19 Task 1 on hierarchical classiﬁcation of blurbs. The challenge is a document clas-siﬁcation task where the hierarchical structure of each document needs to be captured. Our systems achieved the 13th place out of 19 sub-missions for Sub-Task A and the 11th place out of 19 submissions for Sub-Task B. We describe in detail these two systems pointing out the advantages and disadvantages of each as well as laying out future research directions.
Co-Design of Edutainment Applications with Preschoolers. Is it feasible?

Children of nowadays are exposed to technology from their early childhood, thus becoming technology consumers. Creating the appropriate content and interaction for children will determine a better quality of the time they spend with technology. When designing for adults, the participation of the end users guarantees the success of the final product. But is it possible to reproduce the process with small children (3-6/7 ages)? In this paper, we present our approach in involving preschoolers in the design of educational software. The design teams are formed by Computer Science students attending the Human-Computer Interaction optional course. Author
FogDocker: Start Container Now, Fetch Image Later

Slow software deployment is an important issue in environments such as fog computing where this operation lies in the critical path of providing online services to the end users. The problem is even worse when the virtualized resources are made of modest machines such as single-board computers. This paper leverages the observation that, although Docker images are often very large, only a small fraction of their content is actually accessed by the containers during startup. We therefore propose to reorganize container images and download only the strictly necessary files before starting a container. The remaining image contents can then be downloaded asynchronously while the container is already running. Our performance evaluations show that FogDocker reduces container deployment times in the order of 3-5x on single-board computers and 2-3x on powerful servers, while incurring low runtime overhead and maintaining correctness even in the case a container accesses a file which was not downloaded yet.
Identification and Classification of Trucks and Trailers on the Road Network through Deep Learning

Understanding the flow of traffic on road networks is a primary focus of road transport authorities. A range of technologies has been applied to measure throughput and potential congestion on the roads. However, such technologies are currently limited in disambiguating the kinds of vehicles on the road network. Certain vehicles types are especially important to distinguish from other traffic, e.g. trucks and trailers. These are larger vehicles that can have a major impact on the roads and surrounding areas, e.g. due to pollution or the excess noise that they can give rise to. In this paper, we present an approach to automatically detect, classify and count the unique classes of trucks and trailers that are found on the road network of Victoria using approaches based on deep learning. In particular, we consider two leading object detection systems: You Only Look Once version 3 (YOLOv3) and Single Shot Multi-Box Detector (SSD). We compare the models and the performance achieved for truck and trailer classification, including dealing with more common and less common truck and trailer types. We measure the efficiency and accuracy of the approaches based on the mean average precision achieved.
Visualising and evaluating the effects of combining active learning with word embedding features

A tool that enables the use of active learning, as well as the incorporation of word embeddings, was evaluated for its ability to decrease the training data set size required for a named entity recognition model. Uncertainty-based active learning and the use of word embeddings led to very large performance improvements on small data sets for the entity categories PERSON and LOCATION . In contrast, the embedding features used were shown to be unsuitable for detecting entities belonging to the OR - GANISATION category. The tool was also extended with functionality for visualising the usefulness of the active learning process and of the word embeddings used. The visualisations provided were able to indicate the performance differences between the entities, as well as differences with regards to usefulness of the embedding features.
Improving Structure Evaluation Through Automatic Hierarchy Expansion

Structural segmentation is the task of partitioning a recording into non-overlapping time intervals, and labeling each segment with an identifying marker such as A , B , or verse . Hierarchical structure annotation expands this idea to allow an annotator to segment a song with multiple levels of granularity. While there has been recent progress in developing evaluation criteria for comparing two hierarchical annotations of the same recording, the existing methods have known deﬁciencies when dealing with inexact label matchings and sequential label repetition. In this article, we investigate methods for automatically enhancing structural annotations by inferring (and expanding) hierarchical information from the segment labels. The proposed method complements existing techniques for comparing hierarchical structural annotations by coarsening or reﬁning labels with variation markers to either collapse similarly labeled segments together, or separate identically labeled segments from each other. Using the multi-level structure annotations provided in the SALAMI dataset, we demonstrate that automatic hierarchy expansion allows structure comparison methods to more accurately assess similarity between annotations.
Evaluating Off-the-Shelf NLP Tools for German

It is not always easy to keep track of what tools are currently available for a particular annotation task, nor is it obvious how the provided models will perform on a given data set. In this contribution, we provide an overview of the tools available for the automatic annotation of German-language text. We evaluate ﬁfteen free and open source NLP tools for the linguistic annotation of German, looking at the fundamental NLP tasks of sentence segmentation, tokenization, POS tagging, morphological analysis, lemmatization, and dependency parsing. To get an idea of how the systems’ performance will generalize to various domains, we compiled our test corpus from various non-standard domains. All of the systems in our study are evaluated not only with respect to accuracy, but also the computational resources required.
Can We Increase Inter- and Intra-Rater Agreement in Modeling General Music Similarity?

We present a pilot study on ways to increase inter-and intra-rater agreement in quantiﬁcation of general similarity between pieces of music. By using a more controlled group of human subjects and carefully curating song material, we try to increase overall agreement between raters concerning the perceived general similarity of songs. Repeated conduction of the experiment with a two week lag shows that intra-rater agreement is higher than inter-rater agreement. Analysis of the results and interviews with test subjects suggests that the genre of songs was a major factor in judging similarity between songs. We discuss the impacts of our results on evaluation of respective machine learning models and question the validity of experiments on general music similarity.
EFFECTS OF KINESIO TAPING TECHNIQUE IN CHILDREN WITH CEREBRAL PALSY: REVIEW

Background and Aim: Cerebral palsy is the most common movement disorder that is associated with life-long disability and movement impairment and leads to “disabilities” such as impairment, function limitation, and participation restrictions. Developing of the psychomotor disturbances results in the loss of ability to use the limbs, more weakness and paralysis, disability in performing activities of daily living, more dependence and ultimately, lowering quality of life. Therefore, because of developing of such disturbances, early rehabilitation is essential for patients with cerebral palsy. Kinesio taping technique is a relatively new technique applied in rehabilitation programs. The aim of the present review was a discussion about the effects of Kinesio taping techniques on improving motor skills in children with cerebral palsy Materials and Methods: Using the keywords Cerebral palsy, Taping, Kinesio tape, and Kinesio taping electronic databases including Scopus, Pubmed, ISI, and Google Scholar were searched. Finally, 14 articles, based on the inclusion criteria were selected, up to April 2015. The articles were then classified according to the type and site of Kinesio taping application on the body. Results: Review of the literature demonstrated that Kinesio taping technique is more effective on gross and fine motor function and dynamic activities than postural and static activities. Also, it was found that this technique has more effect on the children in higher developmental and motor stages (1 and 2 levels of Gross Motor Function Classification System (GMFCS)). Conclusion: It is concluded that Kinesio taping technique, as an adjunctive treatment and in combination with other treatment techniques, can be effective in the neurological rehabilitation of children with cerebral palsy to improve motor function and dynamic activities, especially in higher developmental and motor stages (1 and 2 levels of GMFCS).
Autoware Toolbox: MATLAB/Simulink Benchmark Suite for ROS-based Self-driving Software Platform

This paper describes a MATLAB/Simulink benchmark suite for an open-source self-driving system based on Robot Operating System (ROS). In recent years, self-driving systems have been developed worldwide, and the technology has been making remarkable progress. One approach to the development of the self-driving systems is the utilization of ROS which is an open-source middleware framework used for developing robot applications. On the other hand, the popular approach in the automotive industry is the utilization of MATLAB/Simulink which is the software for modeling, simulating, and analyzing. MATLAB/Simulink provides an interface between ROS and MATLAB/Simulink that enables to create functionalities of ROS-based robots in MATLAB/Simulink. However, it has not been fully utilized in the development of the self-driving systems yet because there are not enough open-source models for self-driving. Thus the co-development is difficult. Therefore, we provide a MATLAB/Simulink benchmark suite for a ROS-based self-driving system called Autoware. Autoware is popular open-source software that provides a complete set of self-driving modules. The provided benchmark contains MATLAB/Simulink models. They help to design the ROS-based self-driving systems using MATLAB/Simulink. Moreover, we investigated other benchmarks for self-driving and indicated that this is the first work aiming to the ROS-based self-driving systems with MATLAB/Simulink.
Folded CQT RCNN For Real-time Recognition of Instrument Playing Techniques

In the past years, deep learning has produced state-of-the-art performance in timbre and instrument classification. However, only a few models currently deal with the recognition of advanced Instrument Playing Techniques (IPT). None of them have a real-time approach of this problem. Furthermore, most studies rely on a single sound bank for training and testing. Their methodology provides no assurance as to the generalization of their results to other sounds. In this article, we extend state-of-the-art convolutional neural networks to the classification of IPTs. We build the first IPT corpus from independent sound banks, annotate it with the JAMS standard and make it freely available. Our models yield consistently high accuracies on a homogeneous subset of this corpus. However, only a proper taxonomy of IPTs and specifically defined input transforms offer proper resilience when addressing the “ minus-1db ” methodology, which assesses the ability of the models to generalize. In particular, we introduce a novel Folded Constant Q-Transform adjusted to the requirements of IPT classification. Finally we discuss the use of our classifier in real-time.
A Hybrid ICN Caching Strategy Based on Region Division

Information-centric Networking (ICN) has become a promising paradigm towards the future Internet. In-network caching is one of its prominent features. Existing cache schemes roughly group into deterministic caching and probabilistic caching. However, most current studies tend to employ a sole caching strategy throughout the entire network, which is hard to meet both users' and ISPs' demands. We propose a hybrid caching strategy (HCache), which uses different caching methods in different regions and combine their merits. Specifically, the cache network is divided into the core and edge areas. The edge areas apply deterministic caching to reduce access delay from user's angle, while the core area utilizes probabilistic caching to enrich the cache diversity and reduce inter-domain traffic from ISP's angle. Simulations indicate HCache outperforms traditional probabilistic and deterministic caching schemes in terms of access delay, cache hit ratio and bandwidth savings. Moreover, HCache is efficient in content delivery and friendly to both users and ISPs.
Pattern Clustering in Monophonic Music by Learning a Non-Linear Embedding From Human Annotations

Musical pattern discovery algorithms ﬁnd instances of repetition in symbolic music, allowing for some user-speciﬁable amount of variation between identiﬁed repetitions; however, they can yield an intractably large number of discovered patterns when allowing for even small amounts of variation. This is commonly addressed by deﬁning some heuristic notion of pattern signiﬁcance, and returning only the most signiﬁcant patterns. This paper develops a method of pattern discovery that models human judgement of what constitutes a signiﬁcant pattern by incorporating annotations of repeated patterns, avoiding the need to design heuristics. We take pattern discovery as a clustering task, where the input is a set of passages of monophonic music, represented as vectors of extracted features, and the output clusters correspond to discovered patterns. The human annotations are used to train a neural network to learn a low-dimensional embedding of the feature space that maps passages of music close together when they are occurrences of the same ground-truth pattern. The results of this approach match up with the annotations signiﬁcantly better than the results of an approach using clustering without subspace learning. We provide examples of the types of patterns that this method tends to discover and discuss its feasibility and practicality as a tool for extracting useful information about repetitive structure in music.
Label Propagation of Polarity Lexica on Word Vectors

The Semi-supervised learning (SSL) is an important research area in machine learning where both labeled and unlabeled data is used to build a model. One of the big advantages of semi-supervised meth-ods is that they are transparent and easy to comprehend for humans, unlike most deep learning techniques which are black box. In this paper, we design a graph-based semi-supervised learning framework to detect sentiment polarity in word vectors trained on a German corpus. We study theoretical aspects of the task, empirically analyze a seminal label propagation algorithm (Zhu and Ghahramani, 2002) and suggest variants to improve classiﬁcation performance. Additionally, we review the literature of graph construction for SSL and propose new methods to avoid hubs, i.e., vertices of high degree, which are harmful as outlined by Ozaki et al. (2011).
RGCL at GermEval 2019: Offensive Language Detection with Deep Learning

This paper describes the system submitted by the RGCL team to GermEval 2019 Shared Task 2: Identiﬁcation of Offensive Language . We experimented with ﬁve different neural network architectures in order to classify Tweets in terms of offensive language. By means of comparative evaluation, we select the best performing for each of the three subtasks. Overall, we demonstrate that using only minimal pre-processing we are able to obtain competitive results.
Fog Horizons -- A Theoretical Concept to Enable Dynamic Fog Architectures

With the Internet of Things, more and more devices, and therefore data, need to be handled by current architectures. The flood of provided information leads to bottlenecks within regular client-server architectures. In order to address this challenge and to enable new applications, fog computing is a promising architectural style which pushes the data and device handling closer to the edge. Thus, systems are getting more decentralized and -- with the inclusion of devices with constantly changing positions such as cars, drones and smartphones -- more dynamic. To address the corresponding challenges, we defined four concepts for dynamic Fog Architectures: Fog Visibility, Fog Horizon, Fog Reachability, and finally, a redefinition of the Fog Architecture, based on the previous concepts. These concepts support a common understanding of the constantly changing sets of components these fog environments have to deal with. In addition to the common understanding, the concepts are also helpful to set up architectures, discover communication partners as well as setting scopes. We provide mathematical definitions for each concept and give examples. In the case study, we applied these concepts in four applications from different domains to show the applicability and generalizability.
Metaphor detection for German Poetry

This paper presents first steps towards metaphor detection in German poetry, in particular in expressionist poems. We create a dataset with adjective-noun pairs extracted from expressionist poems, manually annotated for metaphoricity. We discuss the annotation process and present models and experiments for metaphor detection where we investigate the impact of context and the domain dependence of the models.
HISDOM: A Hybrid Ontology Mapping System based on Convolutional Neural Network and Dynamic Weight

In the information explosion era, mapping multiple ontologies in different knowledge bases could provide a common layer from which several ontologies could be accessed and exchanged in semantically sound manners. However, most ontology mapping approaches rely heavily on human annotation and are restricted to limited domains. Moreover, most prior work is limited to combining a few mapping factors with moderate mapping accuracy, and the ontology mapping weigh calculation process is not adaptive and dynamic. Based on the current generalization of ontology mapping methods, we propose a novel ontology mapping system called HISDOM to improve the generalization performance of ontology mapping. HISDOM uses comprehensive factors like concept names, attributes, instances, and structural similarities to determine the similarity of ontology. A key novelty of HISDOM is leveraging CNN to calculate the comment similarity to assist the mapping process of concepts in the ontology. Then, HISDOM dynamically derives the weight of different factors in the overall ontology similarity proportional to the amount of information of each factor in the ontology. Finally, based on the overall similarity, HISDOM determines whether the two concepts in the ontology can be mapped. We study the performance of HISDOM through extensive experiments. The results show that HISDOM outperforms several baselines in ontology mapping tasks, the dynamic and adaptive weighting mechanism is effective, and all the mapping factors of HISDOM are positive towards improving the ontology mapping accuracy.