nils-herrmann/clean_scirepeval huggingface dataset:
Audio content processing for automatic music genre classification: descriptors, databases, and classifiers

This dissertation presents, discusses, and sheds some light on the problems that appear when computers try to automatically classify musical genres from audio signals. In particular, a method is proposed for the automatic music genre classification by using a computational approach that is inspired in music cognition and musicology in addition to Music Information Retrieval techniques. In this context, we design a set of experiments by combining the different elements that may affect the accuracy in the classification (audio descriptors, machine learning algorithms, etc.). We evaluate, compare and analyze the obtained results in order to explain the existing glass-ceiling in genre classification, and propose new strategies to overcome it. Moreover, starting from the polyphonic audio content processing we include musical and cultural aspects of musical genre that have usually been neglected in the current state of the art approaches. This work studies different families of audio descriptors related to timbre, rhythm, tonality and other facets of music, which have not been frequently addressed in the literature. Some of these descriptors are proposed by the author and others come from previous existing studies. We also compare machine learning techniques commonly used for classification and analyze how they can deal with the genre classification problem. We also present a discussion on their ability to represent the different classification models proposed in cognitive science. Moreover, the classification results using the machine learning techniques are contrasted with the results of some listening experiments proposed. This comparison drive us to think of a specific architecture of classifiers that will be justified and described in detail. It is also one of the objectives of this dissertation to compare results under different data configurations, that is, using different datasets, mixing them and reproducing some real scenarios in which genre classifiers could be used (huge datasets). As a conclusion, we discuss how the classification architecture here proposed can break the existing glass-ceiling effect in automatic genre classification. To sum up, this dissertation contributes to the field of automatic genre classification: a) It provides a multidisciplinary review of musical genres and its classification; b) It provides a qualitative and quantitative evaluation of families of audio descriptors used for automatic classification; c) It evaluates different machine learning techniques and their pros and cons in the context of genre classification; d) It proposes a new architecture of classifiers after analyzing music genre classification from different disciplines; e) It analyzes the behavior of this proposed architecture in different environments consisting of huge or mixed datasets.
Compact Laterally Radiating Dielectric Resonator Antenna With Small Ground Plane

A unilateral probe-fed rectangular dielectric resonator antenna (DRA) with a very small ground plane is investigated. The small ground plane simultaneously works as an excitation patch that excites the fundamental TE111 mode of the DRA, which is an equivalent magnetic dipole. By combining this equivalent magnetic dipole and the electric dipole of the probe, a lateral radiation pattern can be obtained. This complementary antenna has the same E- and H-Planes patterns with low back radiation. Moreover, the cardioid-shaped pattern can be easily steered in the horizontal plane by changing the angular position of the patch (ground). To verify the idea, a prototype operating in 3.5-GHz long term evolution band (3.4–3.6 GHz) was fabricated and measured, with reasonable agreement between the measured and simulated results obtained. It is found that the measured 15-dB front-to-back-ratio bandwidth is 10.9%.
Inter-turn short circuit fault analysis of PWM inverter fed three-phase induction motor using Finite Element Method

This paper deals with the inter-turn short circuit fault analysis of Pulse Width Modulated (PWM) inverter fed three-phase Induction Motor (IM) using Finite Element Method (FEM). The short circuit in the stator winding of a 3-phase IM start with an inter-turn fault and if left undetected it progresses to a phase-phase fault or phase-ground fault. In main fed IM a popular technique known as Motor Current Signature Analysis (MCSA) is used to detect the inter-turn fault. But if the machine is fed from PWM inverter MCSA fails, due to high frequency inverter switching, the current spectrum will be rich in noise causing the fault detection difficult. An electromagnetic field analysis of inverter fed IM is carried out with 25% and 50% of stator winding inter-turn short circuit fault severity using FEM. The simulation is carried out on a 2.2kW IM using Ansys Maxwell Finite Element Analysis (FEA) tool. Comparisons are made on the various electromagnetic field parameters like flux lines distribution, flux density, radial air gap flux density between a healthy and faulty (25% & 50% severity) IM.
A Case of Branchio-oculo-facial Syndrome.

Branchio-oculo-facial syndrome (BOFS) is a rare, autosomal dominant disorder. It is characterized by distinct craniofacial abnormalities including abnormal location of the ears, aplastic cervical skin lesions, malformed auricles, conductive hearing loss, ocular abnormalities, and cleft lip and palate. Herein, we describe a case of BOFS with persistent aplasia cutis of the neck in a 5-year-old girl.
Joint Flow: Temporal Flow Fields for Multi Person Tracking

In this work we propose an online multi person pose tracking approach which works on two consecutive frames It−1 and It . The general formulation of our temporal network allows to rely on any multi person pose estimation approach as spatial network. From the spatial network we extract image features and pose features for both frames. These features serve as input for our temporal model that predicts Temporal Flow Fields (TFF). These TFF are vector fields which indicate the direction in which each body joint is going to move from frame It−1 to frame It . This novel representation allows to formulate a similarity measure of detected joints. These similarities are used as binary potentials in a bipartite graph optimization problem in order to perform tracking of multiple poses. We show that these TFF can be learned by a relative small CNN network whilst achieving state-of-the-art multi person pose tracking results.
Breaking CAPTCHAs with Convolutional Neural Networks

Turing tests to distinguish humans and computers, called CAPTCHA. Contrary to classical Turing tests, in this case the judge is not a human but a computer. The main purpose of such tests is securing user logins against the dictionary or brute force password guessing, avoiding automated usage of various services, preventing bots from spamming on forums and many others. Typical approaches to solving text-based CAPTCHA automatically are based on a scheme specific pipeline containing hand-designed pre-processing, denoising, segmentation, post processing and optical character recognition. Only the last part, optical character recognition, is usually based on some machine learning algorithm. We present an approach using neural networks and a simple clustering algorithm that consists of only two steps, character localisation and recognition. We tested our approach on 11 different schemes selected to present very diverse security features. We experimentally show that using convolutional neural networks is superior to multi-layered perceptrons.
Actuator Control for the NASA-JSC Valkyrie Humanoid Robot: A Decoupled Dynamics Approach for Torque Control of Series Elastic Robots

This paper discusses the actuator-level control of Valkyrie, a new humanoid robot designed by NASA’s Johnson Space Center in collaboration with several external partners. We focus on several topics pertaining to Valkyrie’s series elastic actuators including control architecture, controller design, and implementation in hardware. A decentralized approach is taken in controlling Valkyrie’s many series elastic degrees of freedom. By conceptually decoupling actuator dynamics from robot limb dynamics, we simplify the problem of controlling a highly complex system and streamline the controller development process compared to other approaches. This hierarchical control abstraction is realized by leveraging disturbance observers in the robot’s joint-level torque controllers. We apply a novel analysis technique to understand the ability of a disturbance observer to attenuate the effects of unmodeled dynamics. The performance of our control approach is demonstrated in two ways. First, we characterize torque tracking performance of a single Valkyrie actuator in terms of controllable torque resolution, tracking error, bandwidth, and power consumption. Second, we perform tests on Valkyrie’s arm, a serial chain of actuators, and demonstrate its ability to accurately track torques with our decentralized control approach.
Uncovering Topic Dynamics of Social Media and News: The Case of Ferguson

Looking at the dynamics of news content and social media content can help us understand the increasingly complex dynamics of the relationship between the media and the public surrounding noteworthy news events. Although topic models such as latent Dirichlet allocation (LDA) are valuable tools, they are a poor fit for analyses in which some documents, like news articles, tend to incorporate multiple topics, while others, like tweets, tend to be focused on just one. In this paper, we propose Single Topic LDA (ST-LDA) which jointly models newstype documents as distributions of topics and tweets as having a single topic; the model improves topic discovery in news and tweets within a unified topic space by removing noisy topics that conventional LDA tends to assign to tweets. Using ST-LDA, we focus on the unrest in Ferguson, Missouri after the fatal shooting of Michael Brown on August 9, 2014, looking in particular at the topic dynamics of tweets in and out of St. Louis area, and at differences and relationships between topic coverage in news and tweets.
Methylation by NSun2 represses the levels and function of microRNA 125b.

Methylation is a prevalent posttranscriptional modification of RNAs. However, whether mammalian microRNAs are methylated is unknown. Here, we show that the tRNA methyltransferase NSun2 methylates primary (pri-miR-125b), precursor (pre-miR-125b), and mature microRNA 125b (miR-125b) in vitro and in vivo. Methylation by NSun2 inhibits the processing of pri-miR-125b2 into pre-miR-125b2, decreases the cleavage of pre-miR-125b2 into miR-125, and attenuates the recruitment of RISC by miR-125, thereby repressing the function of miR-125b in silencing gene expression. Our results highlight the impact of miR-125b function via methylation by NSun2.
Overview of Automated Hematology Analyzer XE-2100

The XE-2100 is a new automated hematology analyzer developed by Sysmex Corporation. With consideration of medical analyzer requirements for the 21st century, this unit was developed to satisfy the following concepts and to cope with diversified laboratory needs. 1) Electronic network communication 2) Presentation of high level value-added information 3) Contribution to test cost control 4) Pursuit of a user-friendly analyzer The XE-2100 provides integrated reticulocyte analysis, previously available only in a separate device, and has the ability to enumerate nucleated red blood cells. The instrumentÕs capacity for high throughput allows simultaneous determination of five-part white blood cell differential, nucleated red blood cell count at the rate of 150 samples per hour. These features contribute to a significant reduction in report turn-around-time (TAT). In addition, the instrument is designed to provide remote maintenance support and on-line quality control reporting through an electronic network, thus improving laboratory management.
A multi-level framework to identify HTTPS services

The development of TLS-based encrypted traffic comes with new challenges related to the management and security analysis of encrypted traffic. There is an essential need for new methods to investigate, with a proper level of identification, the increasing number of HTTPS traffic that may hold security breaches. In fact, although many approaches detect the type of an application (Web, P2P, SSH, etc.) running in secure tunnels, and others identify a couple of specific encrypted web pages through website fingerprinting, this paper proposes a robust technique to precisely identify the services run within HTTPS connections, i.e. to name the services, without relying on specific header fields that can be easily altered. We have defined dedicated features for HTTPS traffic that are used as input for a multi-level identification framework based on machine learning algorithms. Our evaluation based on real traffic shows that we can identify encrypted web services with a high accuracy.
Improvement of fully automated airway segmentation on computed tomographic images using 2 . 5 D and 3 D convolutional neural net

We proposed novel airway segmentation methods in volumetric chest computed tomography (CT) using 2.5D convolutional neural net (CNN) and 3D CNN. A method with 2.5D CNN segments airways by voxel-by-voxel classification based on patches which are from three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal slices around each voxel, while 3D CNN segments by 3D patch-based semantic segmentation using modified 3D U-Net. The extra-validation of our proposed method was demonstrated in 20 test datasets of the EXACT’09 challenge. The detected tree length and the false positive rate was 60.1%, 4.56% for 2.5D CNN and 61.6%, 3.15% for 3D CNN. Our fully automated (end-to-end) segmentation method could be applied in radiological practice.
Classification and comparative evaluation of PV panel integrated DC-DC converter concepts

Strings of photovoltaic panels have a significantly reduced power output when mismatch between the panels, such as partial shading, occurs since integrated diodes are then partly bypassing the shaded panels. With the implementation of DC-DC converters on panel level, the maximum available power can be extracted from each panel regardless of any shading. In this paper, different concepts of PV panel integrated DC-DC converters are presented, comparative evaluation is given and the converter design process is shown for the buck-boost converter which is identified as the best suited concept. Furthermore, the results of high precision efficiency measurements of an experimental prototype are presented and compared to a commercial MIC.
Large-scale neurocognitive networks and distributed processing for attention, language, and memory.

Cognition and comportment are subserved by interconnected neural networks that allow high-level computational architectures including parallel distributed processing. Cognitive problems are not resolved by a sequential and hierarchical progression toward predetermined goals but instead by a simultaneous and interactive consideration of multiple possibilities and constraints until a satisfactory fit is achieved. The resultant texture of mental activity is characterized by almost infinite richness and flexibility. According to this model, complex behavior is mapped at the level of multifocal neural systems rather than specific anatomical sites, giving rise to brain-behavior relationships that are both localized and distributed. Each network contains anatomically addressed channels for transferring information content and chemically addressed pathways for modulating behavioral tone. This approach provides a blueprint for reexploring the neurological foundations of attention, language, memory, and frontal lobe function.
A single-stage isolated bidirectional matrix based AC-DC converter for energy storage

This paper presents a three-phase single-stage bidirectional isolated matrix based AC-DC converter for energy storage. The matrix (3 × 1) topology directly converts the three-phase line voltages into high-frequency AC voltage which is subsequently, processed using a high-frequency transformer followed by a controlled rectifier. A modified Space Vector Modulation (SVM) based switching scheme is proposed to achieve high input power quality with high power conversion efficiency. Compared to the conventional two stage converter, the proposed converter provides single-stage conversion resulting in higher power conversion efficiency and higher power density. The operating principles of the proposed converter in both AC-DC and DC-AC mode are explained followed by steady state analysis. Simulation results are presented for 230 V, 50 Hz to 48 V isolated bidirectional converter at 2 kW output power to validate the theoretical claims.
Using “Omics” and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.
Transporting information and energy simultaneously

The fundamental tradeoff between the rates at which energy and reliable information can be transmitted over a single noisy line is studied. Engineering inspiration for this problem is provided by powerline communication, RFID systems, and covert packet timing systems as well as communication systems that scavenge received energy. A capacity-energy function is defined and a coding theorem is given. The capacity-energy function is a non-increasing concave cap function. Capacity-energy functions for several channels are computed.
Space vector modulation strategy based on volt-second balancing for three-level neutral point clamped inverter

In this paper, a methodology to apply space vector modulation (SVM) to a three-level neutral point clamped (NPC) inverter based on volt-second balancing principle is presented. The proposed scheme, switching-state selection of the three-level NPC inverter by using a nearest three vector scheme, is fully described. A mathematical analysis of the duty cycle is determined by utilizing the calculated switching time method in order to generate the duty cycle signals for both the continuous SVM (CSVM) and the discontinuous SVM (DSVM) conditions. Compared with the CSVM scheme, the DSVM scheme is the reduction in the number of commutations of switches by one third which leads to a further reduction in switching losses. Simulated results are presented to validate the proposal, which demonstrates good steady-state and dynamic performance.
Identifying Vulnerabilities in SCADA Systems via Fuzz-Testing

Security vulnerabilities typically arise from bugs in input validation and in the application logic. Fuzz-testing is a popular security evaluation technique in which hostile inputs are crafted and passed to the target software in order to reveal bugs. However, in the case of SCADA systems, the use of proprietary protocols makes it difficult to apply existing fuzz-testing techniques as they work best when the protocol semantics are known, targets can be instrumented and large network traces are available. This paper describes a fuzz-testing solution involving LZFuzz, an inline tool that provides a domain expert with the ability to effectively fuzz SCADA devices.
Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond

Topic models are a family of statistical-based algorithms to summarize, explore and index large collections of text documents. After a decade of research led by computer scientists, topic models have spread to social science as a new generation of data-driven social scientists have searched for tools to explore large collections of unstructured text. Recently, social scientists have contributed to topic model literature with developments in causal inference and tools for handling the problem of multi-modality. In this paper, I provide a literature review on the evolution of topic modeling including extensions for document covariates, methods for evaluation and interpretation, and advances in interactive visualizations along with each aspect’s relevance and application for social science research. computational social science, computer-assisted text analysis, visual analytics, structural topic model
Index switching causes “ spreading-of-signal ” among multiplexed samples in Illumina HiSeq 4000 DNA sequencing

Illumina-based next generation sequencing (NGS) has accelerated biomedical discovery through its ability to generate thousands of gigabases of sequencing output per run at a fraction of the time and cost of conventional technologies. The process typically involves four basic steps: library preparation, cluster generation, sequencing, and data analysis. In 2015, a new chemistry of cluster generation was introduced in the newer Illumina machines (HiSeq 3000/4000/X Ten) called exclusion amplification (ExAmp), which was a fundamental shift from the earlier method of random cluster generation by bridge amplification on a non-patterned flow cell. The ExAmp peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this  (which was not .  first posted online Apr. 9, 2017;
DTMC Model Checking by SCC Reduction

Discrete-Time Markov Chains (DTMCs) are a widely-used formalism to model probabilistic systems. On the one hand, available tools like PRISM or MRMC offer efficient model checking algorithms and thus support the verification of DTMCs. However, these algorithms do not provide any diagnostic information in the form of counterexamples, which are highly important for the correction of erroneous systems. On the other hand, there exist several approaches to generate counterexamples for DTMCs, but all these approaches require the model checking result for completeness. In this paper we introduce a model checking algorithm for DTMCs that also supports the generation of counterexamples. Our algorithm, based on the detection and abstraction of strongly connected components, offers abstract counterexamples, which can be interactively refined by the user.