nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Music Genre Classification Based on Modulation Spectral Analysis of Spectral and Cepstral Features

In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation subbands. The modulation spectral contrast (MSC) and modulation spectral valley (MSV) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSCs and MSVs of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is employed to improve the classification accuracy. Experiments conducted on two different music datasets have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.
Vertex Stimulation as a Control Site for Transcranial Magnetic Stimulation: A Concurrent TMS/fMRI Study

A common control condition for transcranial magnetic stimulation (TMS) studies is to apply stimulation at the vertex. An assumption of vertex stimulation is that it has relatively little influence over on-going brain processes involved in most experimental tasks, however there has been little attempt to measure neural changes linked to vertex TMS. Here we directly test this assumption by using a concurrent TMS/fMRI paradigm in which we investigate fMRI blood-oxygenation-level-dependent (BOLD) signal changes across the whole brain linked to vertex stimulation.



Thirty-two healthy participants to part in this study. Twenty-one were stimulated at the vertex, at 120% of resting motor threshold (RMT), with short bursts of 1 Hz TMS, while functional magnetic resonance imaging (fMRI) BOLD images were acquired. As a control condition, we delivered TMS pulses over the left primary motor cortex using identical parameters to 11 other participants.



Vertex stimulation did not evoke increased BOLD activation at the stimulated site. By contrast we observed widespread BOLD deactivations across the brain, including regions within the default mode network (DMN). To examine the effects of vertex stimulation a functional connectivity analysis was conducted.



The results demonstrated that stimulating the vertex with suprathreshold TMS reduced neural activity in brain regions related to the DMN but did not influence the functional connectivity of this network. Our findings provide brain imaging evidence in support of the use of vertex simulation as a control condition in TMS but confirm that vertex TMS induces regional widespread decreases in BOLD activation.
Preserving differential privacy in convolutional deep belief networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users’ personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing $$\epsilon $$ ϵ -differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions.
LSTM Deep Neural Networks Postfiltering for Enhancing Synthetic Voices

Recent developments in speech synthesis have produced systems capable of producing speech which closely resembles natural speech, and researchers now strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. Speech synthesis based on Hidden Markov Models (HMM) is of great interest to researchers, due to its ability to produce sophisticated features with a small footprint. Despite some progress, its quality has not yet reached the level of the current predominant unit-selection approaches, which select and concatenate recordings of real speech, and work has been conducted to try to improve HMM-based systems. In this paper, we present an application of long short-term memory (LSTM) deep neural networks as a post ̄ltering step in HMM-based speech synthesis. Our motivation stems from a similar desire to obtain characteristics which are closer to those of natural speech. The paper analyzes four types of post ̄lters obtained using ̄ve voices, which range from a single post ̄lter to enhance all the parameters, to a multi-stream proposal which separately enhances groups of parameters. The di®erent proposals are evaluated using three objective measures and are statistically compared to determine any signi ̄cance between them. The results described in the paper indicate that HMM-based voices can be enhanced using this approach, specially for the multistream post ̄lters on the considered objective measures.
The Skiplist-Based LSM Tree

Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is aractive for write-optimized data systems. Maintaining an ecient buer in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the SkiplistBased LSM Tree (sLSM), a novel system in which the memory buer of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad exibility for excellent performance across a wide variety of workloads.
Variance Reduction for Faster Non-Convex Optimization

We consider the fundamental problem in nonconvex optimization of efficiently reaching a stationary point. In contrast to the convex case, in the long history of this basic problem, the only known theoretical results on first-order nonconvex optimization remain to be full gradient descent that converges in O(1/ε) iterations for smooth objectives, and stochastic gradient descent that converges inO(1/ε) iterations for objectives that are sum of smooth functions. We provide the first improvement in this line of research. Our result is based on the variance reduction trick recently introduced to convex optimization, as well as a brand new analysis of variance reduction that is suitable for non-convex optimization. For objectives that are sum of smooth functions, our first-order minibatch stochastic method converges with an O(1/ε) rate, and is faster than full gradient descent by Ω(n). We demonstrate the effectiveness of our methods on empirical risk minimizations with non-convex loss functions and training neural
Design of a cable-driven active leg exoskeleton (C-ALEX) and gait training experiments with human subjects

Robotic rehabilitation devices are attractive to physical therapists. Various leg exoskeletons have been developed during the past decade and have been used in gait training. Traditional exoskeletons usually have a complex structure and add extra inertia to the wearer's leg, which may change their natural gait. In this paper, we present the design of a cable-driven active leg exoskeleton (C-ALEX) for human gait training. The advantages of cable-driven designs are that they have a simpler structure, add minimal inertia to the human limbs, and do not require precise joint alignment. C-ALEX employs the “assist-as-needed” control strategy to help the ankle center move along a prescribed path. An experiment with 6 healthy subjects was conducted who walked with C-ALEX on a treadmill. The results showed that C-ALEX is capable of helping the subjects better track a prescribed ankle path.
Identification of robust focus measure functions for the automated capturing of focused images from Ziehl-Neelsen stained sputum smear microscopy slide.

Capturing of the best-focused image using focus measure function (FMF) from a stack of images acquired at different focus distances is a crucial step in automatic microscopy development. Detection of bacilli present in Ziehl-Neelsen (ZN) stained sputum smears conventional microscope (CM) images is critical to disease diagnosis. Studies have revealed that the performances of FMFs are sensitive to image contents and background noise. In this article, 24 diverse FMFs were implemented on 31 stacks of CM's field of view images acquired from three different microscopes to determine the best-focused one. Seven FMFs achieved the accuracies of greater than 90%. Accuracy, focus error, and false maxima were calculated for each FMF, and overall score and ranking were also calculated for better interpretation. Preprocessing techniques such as filtering and image distortions (noise, contrast, saturation, illumination, etc.) were performed to evaluate the robustness of every FMF. Gaussian derivative, steerable filters, Tenengrad, and Hemli and Scherer's mean FMFs were identified as the most robust and accurate functions with the accuracy >90%. These FMFs have a relatively less focus error and false maxima rate. Full widths at half maximum of these four FMFs were also computed to determine their efficacy for the optimization process. These four FMFs can be implemented for automated capturing of the image from ZN-stained sputum smear slide. Gaussian derivative FMF can also be used effectively for both CM and fluorescence microscope's field of view image stacks to determine the best-focused one from each stack.
A tactile sensor for the fingertips of the humanoid robot iCub

In order to successfully perform object manipulation, humanoid robots must be equipped with tactile sensors. However, the limited space that is available in robotic fingers imposes severe design constraints. In  we presented a small prototype fingertip which incorporates a capacitive pressure system. This paper shows an improved version, which has been integrated on the hand of the humanoid robot iCub. The fingertip is 14.5 mm long and 13 mm wide. The capacitive pressure sensor system has 12 sensitive zones and includes the electronics to send the 12 measurements over a serial bus with only 4 wires. Each synthetic fingertip is shaped approximately like a human fingertip. Furthermore, an integral part of the capacitive sensor is soft silicone foam, and therefore the fingertip is compliant. We describe the structure of the fingertip, their integration on the humanoid robot iCub and present test results to show the characteristics of the sensor.
Organizational Factors to Support Knowledge Management and Innovation

The purpose of this paper is to analyze how organizational factors such as cultural values, leadership and human resource (HR) practices influence knowledge exploration and exploitation practices and innovation via an empirical study.  From the knowledge-based view of the firm, six hypotheses were established and statistically tested in a sample of 111 Spanish companies belonging to innovative industries. Survey methodology was used with the aim of gathering data regarding knowledge management (KM) practices and certain, related organizational aspects in firms.  This paper provides evidence of a moderating effect of knowledge-centered culture, knowledge-oriented leadership and knowledge-centered HR practices in the relationship between knowledge exploration and exploitation practices and innovation outcomes of companies. In line with previous literature, it is suggested that although KM practices are important on their own for innovation purposes, when certain enablers – organizational factors to overcome human barriers to KM – are properly established, the innovation capacity of the firm can be more successfully exploited.  The research is limited to high rate innovation industries. Future studies will include other industries and a more diverse sample of firms.  The results of this study suggest that managers should place attention on knowledge exploration and exploitation practices along with several organizational enablers in order to achieve high levels of innovation results for the company.  This paper provides new empirical evidence on the relationships between KM, organizational elements such as culture, leadership, HR practices, and innovation in a large sample of firms. To date, the empirical research of these relations has been mainly limited to descriptive case studies and there is thus a lack of empirical evidence with large samples of firms.
Processing and visualization for diffusion tensor MRI

This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.
SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems

Direct methods for visual odometry (VO) have gained popularity for their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, in which established feature-based methods succeed instead. Based on these considerations, we propose a semidirect VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients, but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.
A Deep Relevance Model for Zero-Shot Document Filtering

In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand. As a fundamental task, information filtering therefore becomes a critical necessity. In this paper, we propose a novel deep relevance model for zero-shot document filtering, named DAZER. DAZER estimates the relevance between a document and a category by taking a small set of seed words relevant to the category. With pre-trained word embeddings from a large external corpus, DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process. The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner. Experiments on two document collections of two different tasks (i.e., topic categorization and sentiment analysis) demonstrate that DAZER significantly outperforms the existing alternative solutions, including the state-of-the-art deep relevance ranking models.
The apnea-ECG database

Sleep apnea is a sleep disorder with a high prevalence in the adult male population. Sleep apnea is regarded as an independent risk factor,for cardiovascular sequelae such as ischemic heart attacks and stroke. The diagnosis of sleep apnea requires polysomnographic studies in sleep laboratories with expensive equipment and attending personnel. Sleep apnea can be treated effectively using nasal ventilation therapy (nCPAP). Early recognition and selection of patients with sleep related breathing disorders is an important task. Although it has been suggested that this can be done on the basis of the ECG, careful quantitative studies of the accuracy of such techniques are needed. An annotated database with 70 nighttime ECG recordings has been created to support such studies. The annotations were based on visual scoring of disordered breathing during sleep.
An integrated framework for human activity recognition

This poster presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. Our framework contains simple pre- and post-classification strategies such as class-imbalance correction on the learning data using structure preserving oversampling, leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively, for improved performance. Through evaluation on recent publicly-available OPPORTUNITY activity datasets comprising of a large amount of multi-dimensional, continuous-valued sensory data, we show that our proposed strategies are effective in improving the performance over common techniques such as One Nearest Neighbor (1NN) and Support Vector Machines (SVM). Our framework also shows better performance over sequential probabilistic models, such as Conditional Random Field (CRF) and Hidden Markov Models (HMM) and when these models are used as meta-learners.
A Nonlinear Unified State-Space Model for Ship Maneuvering and Control in a Seaway

This article presents a unified state-space model for ship maneuvering, station-keeping, and control in a seaway. The frequency-dependent potential and viscous damping terms, which in classic theory results in a convolution integral not suited for real-time simulation, is compactly represented by using a state-space formulation. The separation of the vessel model into a low-frequency model (represented by zero-frequency added mass and damping) and a wave-frequency model (represented by motion transfer functions or RAOs), which is commonly used for simulation, is hence made superfluous.
MCMCTS PCG 4 SMB : Monte Carlo Tree Search to Guide Platformer Level Generation

Markov chains are an enticing option for machine learned generation of platformer levels, but offer poor control for designers and are likely to produce unplayable levels. In this paper we present a method for guiding Markov chain generation using Monte Carlo Tree Search that we call Markov Chain Monte Carlo Tree Search (MCMCTS). We demonstrate an example use for this technique by creating levels trained on a corpus of levels from Super Mario Bros. We then present a player modeling study that was run with the hopes of using the data to better inform the generation of levels in future work.
Safe Exploration for Active Learning with Gaussian Processes

In this paper, the problem of safe exploration in the active learning context is considered. Safe exploration is especially important for data sampling from technical and industrial systems, e.g. combustion engines and gas turbines, where critical and unsafe measurements need to be avoided. The objective is to learn data-based regression models from such technical systems using a limited budget of measured, i.e. labelled, points while ensuring that critical regions of the considered systems are avoided during measurements. We propose an approach for learning such models and exploring new data regions based on Gaussian processes (GP’s). In particular, we employ a problem specific GP classifier to identify safe and unsafe regions, while using a differential entropy criterion for exploring relevant data regions. A theoretical analysis is shown for the proposed algorithm, where we provide an upper bound for the probability of failure. To demonstrate the efficiency and robustness of our safe exploration scheme in the active learning setting, we test the approach on a policy exploration task for the inverse pendulum hold up problem.
Feature Selection Using Adaboost for Face Expression Recognition

We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based classifier, the Support Vector Machines (SVM) and identify the advantages of using AdaBoost over SVM in this context. We use histograms of Gabor and Gaussian derivative responses as the appearance features. We apply our approach to the face expression recognition problem where local appearances play an important role. Finally, we show that though SVM performs equally well, AdaBoost feature selection provides a final hypothesis model that can easily be visualized and interpreted, which is lacking in the high dimensional support vectors of the SVM.