nils-herrmann/clean_scirepeval huggingface dataset:
Tempo Detection of Urban Music Using Tatum Grid Non Negative Matrix Factorization

High tempo detection accuracies have been reported for the analysis of percussive, constant-tempo, Western music audio signals. As a consequence, active research in the tempo detection domain has been shifted to yet open tasks like tempo analysis of non-percussive, expressive, or non-western music. Also, tempo detection is included in a large range of music-related software. In DJ software, features like beat-synching or tempo-synchronized sound effects are widely accepted in the DJ community, and their users rely on correct tempo hypothesis as their basis. In this paper, we are evaluating both academic and commercial tempo detection systems on a typical dataset of an urban club music DJ. Based on this evaluation, we identify octave errors as a problem that has not yet been solved. Further, an approach based on non-negative matrix factorization is presented. In its current state it can compete with the state of the art. It further provides a foundation to tackle the octave error issue in future research.
Methods of Dealing with Values Below the Limit of Detection using SAS Carry

Due to limitations of chemical analysis procedures, small concentrations cannot be precisely measured. These concentrations are said to be below the limit of detection (LOD). In statistical analyses, these values are often censored and substituted with a constant value, such as half the LOD, the LOD divided by the square root of 2, or zero. These methods for handling below-detection values results in two distributions, a uniform distribution for those values below the LOD, and the true distribution. As a result, this can produce questionable descriptive statistics depending upon the percentage of values below the LOD. An alternative method uses the characteristics of the distribution of the values above the LOD to estimate the values below the LOD. This can be done with an extrapolation technique or maximum likelihood estimation. An example program using the same data is presented calculating the mean, standard deviation, t-test, and relative difference in the means for various methods and compares the results. The extrapolation and maximum likelihood estimate techniques have smaller error rates than all the standard replacement techniques. Although more computational, these methods produce more reliable descriptive statistics.
Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics

This paper proposes an efficient bitwise solution to the single-channel source separation task. Most dictionary-based source separation algorithms rely on iterative update rules during the run time, which becomes computationally costly especially when we employ an overcomplete dictionary and sparse encoding that tend to give better separation results. To avoid such cost we propose a bitwise scheme on hashed spectra that leads to an efficient posterior probability calculation. For each source, the algorithm uses a partial rank order metric to extract robust features that form a binarized dictionary of hashed spectra. Then, for a mixture spectrum, its hash code is compared with each source's hashed dictionary in one pass. This simple voting-based dictionary search allows a fast and iteration-free estimation of ratio masking at each bin of a signal spectrogram. We verify that the proposed BitWise Source Separation (BWSS) algorithm produces sensible source separation results for the single-channel speech denoising task, with 6–8 dB mean SDR. To our knowledge, this is the first dictionary based algorithm for this task that is completely iteration-free in both training and testing.
Design of a tunable oscillator using a suspended-stripline resonator

In this paper, a new design method of a tunable oscillator using a suspended-stripline resonator is presented. The negative resistance of an FET mounted on microstrip line (MSL) is combined with a high Q suspended-stripline (SSL) resonator to produce a tunable oscillator with good phase noise. The new MSL-to-SSL transition facilitates easy connection between the MSL-based circuits and the SSL module. The proposed oscillator is also frequency-tunable using a tuner located on the top of the SSL housing. The measured phase noise of the implemented oscillator at 5.148 GHz is -104.34 dBc@100 kHz and -133.21 dBc@1 MHz with 125.7 MHz of frequency tuning.
A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets

The present work describes a classification schema for irony detection in Greek political tweets. Our hypothesis states that humorous political tweets could predict actual election results. The irony detection concept is based on subjective perceptions, so only relying on human-annotator driven labor might not be the best route. The proposed approach relies on limited labeled training data, thus a semi-supervised approach is followed, where collective-learning algorithms take both labeled and unlabeled data into consideration. We compare the semi-supervised results with the supervised ones from a previous research of ours. The hypothesis is evaluated via a correlation study between the irony that a party receives on Twitter, its respective actual election results during the Greek parliamentary elections of May 2012, and the difference between these results and the ones of the preceding elections of 2009. & 2016
Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision

A new image-based approach for fast and robust vehicle tracking from a moving platform is presented. Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle.
Client Clustering for Hiring Modeling in Work Marketplaces

An important problem that online work marketplaces face is grouping clients into clusters, so that in each cluster clients are similar with respect to their hiring criteria. Such a separation allows the marketplace to "learn" more accurately the hiring criteria in each cluster and recommend the right contractor to each client, for a successful collaboration. We propose a Maximum Likelihood definition of the "optimal" client clustering along with an efficient Expectation-Maximization clustering algorithm that can be applied in large marketplaces. Our results on the job hirings at oDesk over a seven-month period show that our client-clustering approach yields significant gains compared to "learning" the same hiring criteria for all clients. In addition, we analyze the clustering results to find interesting differences between the hiring criteria in the different groups of clients.
Tensor Decompositions: A New Concept in Brain Data Analysis?

Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent tech niques for linear and multilinear blind source separation (BSS), especia lly multiway Independent Component Analysis (ICA), Nonnegative Matrix and Tensor Factorization (NMF /NTF), Smooth Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover, tensor decompo sitions have many other potential applications beyond multilinear BSS, especially feature extraction, classification, dimensionality reductio n and multiway clustering. In this paper, we briefly overview new and emerging mo dels and approaches for tensor decompositions in applications to gr oup and linked multiway BSS/ICA, feature extraction, classification and Multiway Parti l Least Squares (MPLS) regression problems. key words: Multilinear BSS, linked multiway BSS /ICA, tensor factorizations and decompositions, constrained Tucker and CP models , P nalized Tensor Decompositions (PTD), feature extraction, classifi cat on, multiway PLS and CCA.
Robotic Strong's procedure for the treatment of superior mesenteric artery syndrome. Description of surgical technique on occasion of the first reported case in the literature.

BACKROUND
Superior Mesenteric Artery Syndrome (SMAS) is a rare disorder caused by compression of the third portion of the duodenum by the SMA. Once a conservative approach fails, usual surgical strategies include Duodenojejunostomy and Strong's procedure. The latter avoids potential anastomotic risks and complications. Robotic Strong's procedure (RSP) combines both the benefits of a minimal invasive approach and also enchased robotic accuracy and efficacy.



For a young girl who was unsuccessfully treated conservatively, the paper describes the RSP surgical technique. To the authors' knowledge, this is the first report in the literature.



Minimal blood loss, short operative time, short hospital stay and early recovery were the short-term benefits. Significant weight gain was achieved three months after the surgery.



Based on primary experience, it is suggested that RSP is a very effective alternative in treating SMAS.
Matters of Consequence : An Empirical Investigation of the WAIS-III and WAIS-IV and Implications for Addressing the Atkins Intelligence Criterion

“Which test provides the better measurement of intelligence, the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) or the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV)?” is an important question to professional psychologists; however, it has become a critical issue in Atkins cases wherein courts are often presented with divergent Full-Scale IQ (FSIQ) scores on the WAIS-III and WAIS-IV. In these instances, courts are required to render a decision stating which test provided the better measure of an inmate’s intellectual functioning. This study employed structural equation modeling to empirically determine which instrument; the WAIS-III or the WAIS-IV, provides the better measure of intelligence via the FSIQ score. Consistent with the publisher’s representation of intellectual functioning, the results from this study indicate the WAIS-IV provides superior measurement, scoring, and structural models to measure FSIQ when compared to the WAIS-III.
Effectiveness of structured hourly nurse rounding on patient satisfaction and clinical outcomes.

Structured hourly nurse rounding is an effective method to improve patient satisfaction and clinical outcomes. This program evaluation describes outcomes related to the implementation of hourly nurse rounding in one medical-surgical unit in a large community hospital. Overall Hospital Consumer Assessment of Healthcare Providers and Systems domain scores increased with the exception of responsiveness of staff. Patient falls and hospital-acquired pressure ulcers decreased during the project period.
An enhancement to SePeCloud with improved security and efficient data management

Outsourcing data to a third-party controlled cloud computing services arises various security issues. High security schemes are required to protect data in the cloud platform. Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) method addressed these issues by improving the security and performance of a cloud environment.  This paper improves the security and data storage with Self-Destruction mechanism by handling the applications independently built on reinforcement learning strategy. Data chunk fingerprint index and sketch index are included to support independent and parallel data destruction among multiple applications. Additionally, SePeCloud is further extended to prevent impersonation attacks from illegitimate users by introducing Modified Shamir secrete sharing scheme to handle user revocation policies with limited storage space. The experimental results prove that the final version of the SePeCloud performed better than the previous versions in terms of Replication cost savings and Computation time by improving both security and performance of the cloud system.
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?

The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurring classes. This is largely due to the fact that most classifiers are designed to maximize accuracy. In many instances, such as for medical diagnosis, this classification behavior is unacceptable because the minority class is the class of primary interest (i.e., it has a much higher misclassification cost than the majority class). In this paper we compare three methods for dealing with data that has a skewed class distribution and nonuniform misclassification costs. The first method incorporates the misclassification costs into the learning algorithm while the other two methods employ oversampling or undersampling to make the training data more balanced. In this paper we empirically compare the effectiveness of these methods in order to determine which produces the best overall classifier—and under what circumstances.
Requirements interaction management

Requirements interaction management (RIM) is the set of activities directed toward the discovery, management, and disposition of critical relationships among sets of requirements, which has become a critical area of requirements engineering. This survey looks at the evolution of supporting concepts and their related literature, presents an issues-based framework for reviewing processes and products, and applies the framework in a review of RIM state-of-the-art. Finally, it presents seven research projects that exemplify this emerging discipline.
Why grit requires perseverance and passion to positively predict performance.

Prior studies linking grit-defined as perseverance and passion for long-term goals-to performance are beset by contradictory evidence. As a result, commentators have increasingly declared that grit has limited effects. We propose that this inconsistent evidence has occurred because prior research has emphasized perseverance and ignored, both theoretically and empirically, the critical role of passion, which we define as a strong feeling toward a personally important value/preference that motivates intentions and behaviors to express that value/preference. We suggest that combining the grit scale-which only captures perseverance-with a measure that assesses whether individuals attain desired levels of passion will predict performance. We first metaanalyzed 127 studies (n = 45,485) that used the grit scale and assessed performance, and found that effect sizes are larger in studies where participants were more passionate for the performance domain. Second, in a survey of employees matched to supervisor-rated job performance (n = 422), we found that the combination of perseverance, measured through the grit scale, and passion attainment, measured through a new scale, predicted higher performance. A final study measured perseverance and passion attainment in a sample of students (n = 248) and linked these to their grade-point average (GPA), finding that the combination of perseverance and passion attainment predicted higher GPAs in part through increased immersion. The present results help resolve the mixed evidence of grit's relationship with performance by highlighting the important role that passion plays in predicting performance. By adequately measuring both perseverance and passion, the present research uncovers grit's true predictive power.
Opportunistic relaying in wireless body area networks: Coexistence performance

In this paper, a cooperative two-hop communication scheme, together with opportunistic relaying (OR), is applied within a mobile wireless body area network (WBAN). Its effectiveness in interference mitigation is investigated in a scenario where there are multiple closely-located networks. Due to a typical WBAN's nature, no coordination is used among different WBANs. A suitable time-division-multiple-access (TDMA) scheme is adopted as both an intra-network and also an internetwork access scheme. Extensive on-body and off-body channel gain measurements are employed to gauge performance, which are overlaid to simulate a realistic WBAN working environment. It is found that opportunistic relaying is able to improve the signal-to-interference-plus-noise ratio (SINR) performance at an outage probability of 10% by an average of 5 dB, and it is also shown that it can reduce level crossing rate (LCR) significantly at low SINRs. Furthermore, this scheme is more efficient when on-body channels fade more rapidly.
FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices

Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.
The impact of IMF conditionality on government health expenditure: A cross-national analysis of 16 West African nations.

How do International Monetary Fund (IMF) policy reforms-so-called 'conditionalities'-affect government health expenditures? We collected archival documents on IMF programmes from 1995 to 2014 to identify the pathways and impact of conditionality on government health spending in 16 West African countries. Based on a qualitative analysis of the data, we find that IMF policy reforms reduce fiscal space for investment in health, limit staff expansion of doctors and nurses, and lead to budget execution challenges in health systems. Further, we use cross-national fixed effects models to evaluate the relationship between IMF-mandated policy reforms and government health spending, adjusting for confounding economic and demographic factors and for selection bias. Each additional binding IMF policy reform reduces government health expenditure per capita by 0.248 percent (95% CI -0.435 to -0.060). Overall, our findings suggest that IMF conditionality impedes progress toward the attainment of universal health coverage.
Ask, Acquire, and Attack: Data-Free UAP Generation Using Class Impressions

Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbations (UAP) that can affect inference of the models over most input samples. Given a model, there exist broadly two approaches to craft UAPs: (i) data-driven: that require data, and (ii) data-free: that do not require data samples. Data-driven approaches require actual samples from the underlying data distribution and craft UAPs with high success (fooling) rate. However, data-free approaches craft UAPs without utilizing any data samples and therefore result in lesser success rates. In this paper, for data-free scenarios, we propose a novel approach that emulates the effect of data samples with class impressions in order to craft UAPs using data-driven objectives. Class impression for a given pair of category and model is a generic representation (in the input space) of the samples belonging to that category. Further, we present a neural network based generative model that utilizes the acquired class impressions to learn crafting UAPs. Experimental evaluation demonstrates that the learned generative model, (i) readily crafts UAPs via simple feed-forwarding through neural network layers, and (ii) achieves state-of-the-art success rates for data-free scenario and closer to that for data-driven setting without actually utilizing any data samples.
A trainable system for object detection in images and video sequences

This thesis presents a general, trainable system for object detection in static images and video sequences. The core system nds a certain class of objects in static images of completely unconstrained, cluttered scenes without using motion, tracking, or handcrafted models and without making any assumptions on the scene structure or the number of objects in the scene. The system uses a set of training data of positive and negative example images as input, transforms the pixel images to a Haar wavelet representation, and uses a support vector machine classi er to learn the di erence between in-class and out-of-class patterns. To detect objects in out-of-sample images, we do a brute force search over all the subwindows in the image. This system is applied to face, people, and car detection with excellent results. For our extensions to video sequences, we augment the core static detection system in several ways { 1) extending the representation to ve frames, 2) implementing an approximation to a Kalman lter, and 3) modeling detections in an image as a density and propagating this density through time according to measured features. In addition, we present a real-time version of the system that is currently running in a DaimlerChrysler experimental vehicle. As part of this thesis, we also present a system that, instead of detecting full patterns, uses a component-based approach. We nd it to be more robust to occlusions, rotations in depth, and severe lighting conditions for people detection than the full body version. We also experiment with various other representations including pixels and principal components and show results that quantify how the number of features, color, and gray-level a ect performance. c Massachusetts Institute of Technology 2000