nils-herrmann/clean_scirepeval huggingface dataset:
ECG Signal Denoising By Wavelet Transform Thresholding

In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. In this paper a new approach based on the threshold value of ECG signal determination is proposed using Wavelet Transform coefficients. Electrocardiography has had a profound influence on the practice of medicine. The electrocardiogram signal contains an important amount of information that can be exploited in different manners. The ECG signal allows for the analysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals are used to verify the proposed method using MATLAB software. Method presented in this paper is compared with the Donoho's method for signal denoising meanwhile better results are obtained for ECG signals by the proposed algorithm.
The Evolution of Constructivist Learning Environments: Immersion in Distributed, Virtual Worlds.

where he has a joint appointment in the Schools of Information Technology & Engineering and of Education. He is Director of Federal Relations and Strategic Alliances for the university and is on the core advisory faculty of GMU's Institute for Public Policy. His professional service centers on organizational redesign for increased effectiveness based on sophisticated technologies. His research interests span technology forecasting and assessment, artificial intelligence, virtual reality, and strategic planning.
The GPU-based Parallel Ant Colony System

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Travelling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.
Android based Automated Irrigation System using Raspberry Pi

Nowadays, adopting an optimized irrigation system has become a necessity due to the lack of the world water resource. The system has a distributed wireless network of soil-moisture and temperature sensors. This project focuses on a smart irrigation system which is cost effective. As the technology is growing and changing rapidly, Wireless sensing Network (WSN) helps to upgrade the technology where automation is playing important role in human life. Automation allows us to control various appliances automatically. DC motor based vehicle is designed for irrigation purpose. The objectives of this paper were to control the water supply to each plant automatically depending on values of temperature and soil moisture sensors. Mechanism is done such that soil moisture sensor electrodes are inserted in front of each soil. It also monitors the plant growth using various parameters like height and width. Android app.
Continuous-Time Mean-Variance Portfolio Selection : A Stochastic LQ Framework

This paper is concerned with a continuous-time mean-variance portfolio selection model that is formulated as a bicriteria optimization problem. The objective is to maximize the expected terminal return and minimize the variance of the terminal wealth. By putting weights on the two criteria one obtains a single objective stochastic control problem which is however not in the standard form due to the variance term involved. It is shown that this nonstandard problem can be “embedded” into a class of auxiliary stochastic linear-quadratic (LQ) problems. The stochastic LQ control model proves to be an appropriate and effective framework to study the mean-variance problem in light of the recent development on general stochastic LQ problems with indefinite control weighting matrices. This gives rise to the efficient frontier in a closed form for the original portfolio selection problem.
Security and Privacy in Device-to-Device (D2D) Communication: A Review

Device-to-device (D2D) communication presents a new paradigm in mobile networking to facilitate data exchange between physically proximate devices. The development of D2D is driven by mobile operators to harvest short range communications for improving network performance and supporting proximity-based services. In this paper, we investigate two fundamental and interrelated aspects of D2D communication, security and privacy, which are essential for the adoption and deployment of D2D. We present an extensive review of the state-of-the-art solutions for enhancing security and privacy in D2D communication. By summarizing the challenges, requirements, and features of different proposals, we identify lessons to be learned from existing studies and derive a set of “best practices.” The primary goal of our work is to equip researchers and developers with a better understanding of the underlying problems and the potential solutions for D2D security and privacy. To inspire follow-up research, we identify open problems and highlight future directions with regard to system and communication design. To the best of our knowledge, this is the first comprehensive review to address the fundamental security and privacy issues in D2D communication.
Short-term real-time traffic prediction methods: A survey

Short-term traffic prediction provides tools for improved road management by allowing the reduction of delays, incidents and other unexpected events. Different real-time approaches provide traffic managers with varying but valuable information. This paper reviews the literature regarding model-driven and data-driven approaches focusing on short-term realtime traffic prediction. We start by analyzing real-time traffic data collection, referring network state acquisition and description methods which are used as input to predictive algorithms. According to the input variables available, we describe common and useful traffic prediction outputs that should contribute to understand the panorama verified on a road network. We then discuss metrics commonly used to assess prediction accuracy, in order to understand a standardized way to compare the different approaches. We list, detail and compare existing model-driven and data-driven approaches that provide short-term real-time traffic predictions. This research leads to an understanding of the many advantages, disadvantages and trade-offs of the approaches studied and provides useful insights for future development. Despite the predominance of model-driven solutions for the last years, data-driven approaches also present good results suitable for Traffic Management usage.
Measuring Peer Pressure , Popularity , and Conformity in Adolescent Boys and Girls : Predicting School Performance , Sexual Attitudes , and Substance Abuse

Existing measures of peer pressure and conformity may not be suitable for screening large numbers of adolescents efficiently, and few studies have differentiated peer pressure from theoretically related constructs, such as conformity or wanting to be popular. We developed and validated short measures of peer pressure, peer conformity, and popularity in a sample ( n= 148) of adolescent boys and girls in grades 11 to 13. Results showed that all measures constructed for the study were internally consistent. Although all measures of peer pressure, conformity, and popularity were intercorrelated, peer pressure and peer conformity were stronger predictors of risk behaviors than measures assessing popularity, general conformity, or dysphoria. Despite a simplified scoring format, peer conformity vignettes were equal to if not better than the peer pressure measures in predicting risk behavior. Findings suggest that peer pressure and peer conformity are potentially greater risk factors than a need to be popular, and that both peer pressure and peer conformity can be measured with short scales suitable for large-scale testing.
Transferring Visual Prior for Online Object Tracking

Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.
Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks

spread of misinformation, rumors and hoaxes. The goal of this work is to introduce a simple modeling framework to study the diffusion of hoaxes and in particular how the availability of debunking information may contain their diffusion. As traditionally done in the mathematical modeling of information diffusion processes, we regard hoaxes as viruses: users can become infected if they are exposed to them, and turn into spreaders as a consequence. Upon verification, users can also turn into non-believers and spread the same attitude with a mechanism analogous to that of the hoax-spreaders. Both believers and non-believers, as time passes, can return to a susceptible state. Our model is characterized by four parameters: spreading rate, gullibility, probability to verify a hoax, and that to forget one's current belief. Simulations on homogeneous, heterogeneous, and real networks for a wide range of parameters values reveal a threshold for the fact-checking probability that guarantees the complete removal of the hoax from the network. Via a mean field approximation, we establish that the threshold value does not depend on the spreading rate but only on the gullibility and forgetting probability. Our approach allows to quantitatively gauge the minimal reaction necessary to eradicate a hoax.
A new PWM/PFM control technique for improving efficiency over wide load range

This paper proposed a novel controlling technique of pulse width modulation (PWM) mode and pulse frequency modulation (PFM) mode to keep the high efficiency within width range of loading. The novel control method is using PWM and PFM detector to achieve two modes switching appropriately. The controlling technique can make the efficiency of current mode DC-DC buck converter up to 88% at light loading and this paper is implemented by TSMC 0.35 mum CMOS process.
Alzheimer disease in the US population: prevalence estimates using the 2000 census.

Current and future estimates of Alzheimer disease (AD) are essential for public health planning.



To provide prevalence estimates of AD for the US population from 2000 through 2050.



Alzheimer disease incidence estimates from a population-based, biracial, urban study, using a stratified random sampling design, were converted to prevalence estimates and applied to US Census Bureau estimates of US population growth.



A geographically defined community of 3 adjacent neighborhoods in Chicago, Ill, applied to the US population.



Alzheimer disease incidence was measured in 3838 persons free of AD at baseline; 835 persons were evaluated for disease incidence.  Current and future estimates of prevalence of clinically diagnosed AD in the US population.



In 2000, there were 4.5 million persons with AD in the US population. By 2050, this number will increase by almost 3-fold, to 13.2 million. Owing to the rapid growth of the oldest age groups of the US population, the number who are 85 years and older will more than quadruple to 8.0 million. The number who are 75 to 84 years old will double to 4.8 million, while the number who are 65 to 74 years old will remain fairly constant at 0.3 to 0.5 million.



The number of persons with AD in the US population will continue to increase unless new discoveries facilitate prevention of the disease.
Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks

The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma. However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice. In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity. Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics. Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed.
Single-microphone wind noise reduction by adaptive postfiltering

This paper presents a novel time-domain algorithm for detecting and attenuating the acoustic effect of wind noise in speech signals originating from mobile terminals. The detection part makes use of metrics that exploits the properties of the spectral envelop of wind noise as well as its non-periodic and non-harmonic nature. LPC analyses of various orders are carried out and the results used to distinguish between wind and speech frames and to estimate the magnitude and location of the wind noise ‘resonance’. The suppression part entails constructing a parameterized postfilter of an appropriate order having a ‘null’ where the wind noise ‘resonance’ is. Wind-only frames are used to estimate the wind noise energy, from which the emphasis parameters of the post-filter are adjusted to provide an appropriate attenuation. The proposed scheme may be combined with background-noise suppression algorithms, or with speech-formant-enhancing post-filters in the context of a speech codec.
Mining activity clusters from low-level event logs

Process mining techniques have proven to be a valuable tool for analyzing the execution of business processes. They rely on logs that identify events at an activity level, i.e., most process mining techniques assume that the information system explicitly supports the notion of activities/tasks. This is often not the case and only low-level events are being supported and logged. For example, users may provide different pieces of data which together constitute a single activity. The technique introduced in this paper uses clustering algorithms to derive activity logs from lower-level data modification logs, as produced by virtually every information system. This approach was implemented in the context of the ProM framework and its goal is to widen the scope of processes that can be analyzed using existing process mining techniques.
Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities

Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven questionanswering (cQA) dataset and the forum dataset, we present a novel learning strategy to promote the performance of our method on the social community datasets without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.
Depth Separation for Neural Networks

Let f : Sd−1 × Sd−1 → R be a function of the form f(x,x′) = g(〈x,x′〉) for g : [−1, 1] → R. We give a simple proof that shows that poly-size depth two neural networks with (exponentially) bounded weights cannot approximate f whenever g cannot be approximated by a low degree polynomial. Moreover, for many g’s, such as g(x) = sin(πdx), the number of neurons must be 2 . Furthermore, the result holds w.r.t. the uniform distribution on Sd−1 × Sd−1. As many functions of the above form can be well approximated by poly-size depth three networks with polybounded weights, this establishes a separation between depth two and depth three networks w.r.t. the uniform distribution on Sd−1 × Sd−1.
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

Identification of protein–protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization–time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein–protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein–protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.
A novel clustering-based approach of indoor location fingerprinting

This study proposes a clustering-based Wi-Fi fingerprinting localization algorithm. The proposed algorithm first presents a novel support vector machine based clustering approach, namely SVM-C, which uses the margin between two canonical hyperplanes for classification instead of using the Euclidean distance between two centroids of reference locations. After creating the clusters of fingerprints by SVM-C, our positioning system embeds the classification mechanism into a positioning task and compensates for the large database searching problem. The proposed algorithm assigns the matched cluster surrounding the test sample and locates the user based on the corresponding cluster's fingerprints to reduce the computational complexity and remove estimation outliers. Experimental results from realistic Wi-Fi test-beds demonstrated that our approach apparently improves the positioning accuracy. As compared to three existing clustering-based methods, K-means, affinity propagation, and support vector clustering, the proposed algorithm reduces the mean localization errors by 25.34%, 25.21%, and 26.91%, respectively.
Scaling up the Naive Bayesian Classifier : Using Decision Trees for Feature Selection

It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5 would use in its decision tree when learning a small example of a training set, a combination of the two different natures of classifiers. Experiments conducted on ten datasets indicate that SBC performs reliably better than NB on all domains, and SBC outperforms C4.5 on many datasets of which C4.5 outperform NB. Augmented Bayesian classifier (ABC) are also tested on the same data, and SBC appears to perform as well as ABC. SBC also can eliminate, on most cases, more than half of the original attributes, which can greatly reduce the size of the training and test data, as well as the running time. Further, the SBC algorithm typically learns faster than both C4.5 and NB, needing fewer training examples to reach high accuracy of classification.
Anthropometry-based structural design of a hand exoskeleton for rehabilitation

We propose a novel exoskeleton for grasping hand rehabilitation based on anthropometry. The proposed design has one degree of freedom (DOF) for each finger, yielding coordinated movement across the distal interphalangeal (DIP), proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joints for each finger. The dimension of each segment is determined by hand anthropometric data obtained from measurements. Each finger is controlled by one motor to allow for independent movement of each finger, which is fundamental for hand dexterity. The design was guided by a proposed mechanical model (the exo-finger model) which was verified by simulation and validated by the movement recorded by prototype fingers. It is concluded that, in the present study, anthropometry-based structural design provides a framework for the development of exoskeletal robotic devices.
Web Spam Detection with Anti-Trust Rank

Spam pages on the web use various techniques to artificially achieve high rankings in search engine results. Human experts can do a good job of identifying spam pages and pages whose information is of dubious quality, but it is practically infeasible to use human effort for a large number of pages. Similar to the approach in , we propose a method of selecting a seed set of pages to be evaluated by a human. We then use the link structure of the web and the manually labeled seed set, to detect other spam pages. Our experiments on the WebGraph dataset [3] show that our approach is very effective at detecting spam pages from a small seed set and achieves higher precision of spam page detection than the Trust Rank algorithm, apart from detecting pages with higher pageranks, on an average.