nils-herrmann/clean_scirepeval huggingface dataset:
Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib' System of Paris

Today, more and more bicycle sharing systems (BSSs) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomenon linked to city dynamics. This article presents a statistical model to automatically analyze the trip data of a bike sharing system. The proposed solution partitions (i.e., clusters) the stations according to their usage profiles. To do so, count series describing the stations’s usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor that handles the differences between the stations’s global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors that shape the geography of trips, and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas that induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris Vélib’ large-scale bike sharing system.
Federated Multi-Task Learning

Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets.
Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG

The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance. In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet. The feature-based classifier obtained an Fi score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.
Detecting credit card fraud by genetic algorithm and scatter search

In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong classification of each transaction do not have the same effect in that if a card is in the hand of fraudsters its whole available limit is used up. Thus, the misclassification cost should be taken as the available limit of the card. This is what we aim at minimizing in this study. As for the solution method, we suggest a novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search. The method is applied to real data and very successful results are obtained compared to current practice.
Deep TEN: Texture Encoding Network

We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries, encoding representation and the classifier are all learned simultaneously. The representation is orderless and therefore is particularly useful for material and texture recognition. The Encoding Layer generalizes robust residual encoders such as VLAD and Fisher Vectors, and has the property of discarding domain specific information which makes the learned convolutional features easier to transfer. Additionally, joint training using multiple datasets of varied sizes and class labels is supported resulting in increased recognition performance. The experimental results show superior performance as compared to state-of-the-art methods using gold-standard databases such as MINC-2500, Flickr Material Database, KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The source code for the complete system are publicly available1.
Pose and Motion from Contact

In the absence of vision, grasping an object often relies on tactile feedback from the ngertips. As the nger pushes the object, the ngertip can feel the contact point move. If the object is known in advance, from this motion the nger may infer the location of the contact point on the object and thereby the object pose. This paper primarily investigates the problem of determining the pose (orientation and position) and motion (velocity and angular velocity) of a planar object with known geometry from such contact motion generated by pushing. A dynamic analysis of pushing yields a nonlinear system that relates through contact the object pose and motion to the nger motion. The contact motion on the ngertip thus encodes certain information about the object pose. Nonlinear observability theory is employed to show that such information is su cient for the nger to \observe" not only the pose but also the motion of the object. Therefore a sensing strategy can be realized as an observer of the nonlinear dynamical system. Two observers are subsequently introduced. The rst observer, based on the result of , has its \gain" determined by the solution of a Lyapunov-like equation; it can be activated at any time instant during a push. The second observer, based on Newton's method, solves for the initial (motionless) object pose from three intermediate contact points during a push. Under the Coulomb friction model, the paper copes with support friction in the plane and/or contact friction between the nger and the object. Extensive simulations have been done to demonstrate the feasibility of the two observers. Preliminary experiments (with an Adept robot) have also been conducted. A contact sensor has been implemented using strain gauges.
Integrating Function , Geometry , Appearance for Scene Parsing

In this paper, we present a Stochastic Scene Grammar (SSG) for parsing 2D indoor images into 3D scene layouts. Our grammar model integrates object functionality, 3D object geometry, and their 2D image appearance in a Function-Geometry-Appearance (FGA) hierarchy. In contrast to the prevailing approach in the literature which recognizes scenes and detects objects through appearance-based classification using machine learning techniques, our method takes a different perspective to scene understanding and recognizes objects and scenes by reasoning their functionality. Functionality is an essential property which often defines the categories of objects and scenes, and decides the design of geometry and scene layout. For example, a sofa is for people to sit comfortably, and a kitchen is a space for people to prepare food with various objects. Our SSG formulates object functionality and contextual relations between objects and imagined human poses in a joint probability distribution in the FGA hierarchy. The latter includes both functional concepts (the scene category, functional groups, functional objects, functional parts) and geometric entities (3D/2D/1D shape primitives). The decomposition of the grammar is terminated on the bottom-up detected lines and regions. We use a Markov chain Monte Carlo (MCMC) algorithm to optimize the Bayesian a posteriori probability and the output parse tree includes a 3D description of the 2D image in the FGA hierarchy. Experimental results on two Yibiao Zhao University of California, Los Angeles (UCLA), USA E-mail: ybzhao@ucla.edu www.yibiaozhao.com Song-Chun Zhu University of California, Los Angeles (UCLA), USA E-mail: sczhu@stat.ucla.edu http://www.stat.ucla.edu/~sczhu challenging indoor datasets demonstrate that the proposed approach not only significantly widens the scope of indoor scene parsing from traditional scene segmentation, labeling, and 3D reconstruction to functional object recognition, but also yields improved overall performance.
Applying Theory of Perceived Risk and Technology Acceptance Model in the Online Shopping Channel A

As the advancement of technology, online shopping channel develops rapidly in recent years. According to the report of Taiwan Network Information Center, there are almost eighty percents of internet population shopping in online channel. Synthesizing insights from the previous research, this study develops the conceptual model to integrate Theory of Perceived Risk (TPR) and Technology Acceptance Model (TAM) to apply in online shopping. Using data collected from 637 respondents from online survey website, we use structural equation modeling to test measurement and structural models. The results suggest the need for consideration of perceived risk as an antecedent in the Technology Acceptance Model. The limitations and implications are discussed. perceived risk, perceived usefulness, perceived ease of use, behavioral intention, actual purchase behavior
Bayesian Nonparametric Poisson Factorization for Recommendation Systems

We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an efficient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with fixed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.
Optical Character Recognition for Cursive Handwriting

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Recognition of Handwritten text has been one of the active and challenging areas of research in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, historical document recognition, bank cheques. In this paper we focus on recognition of cursive English alphabet in a given scanned text document with the help of SVM. The first step is image acquisition which acquires the scanned image followed by normalization of scanned image followed by feature extraction, then making image suitable for segmentation where image is decomposed into sub images and finally each character classification using SVM classifier. Feature Extraction improves recognition rate by making all the characters into a common height. A new combination methods have been developed and has been used for this recognition, which is implemented in MATLAB 2013 a.
The Best of Both Worlds: Learning Geometry-based 6D Object Pose Estimation

We address the task of estimating the 6D pose of known rigid objects, from RGB and RGB-D input images, in scenarios where the objects are heavily occluded. Our main contribution is a new modular processing pipeline. The first module localizes all known objects in the image via an existing instance segmentation network. The next module densely regresses the object surface positions in its local coordinate system, using an encoder-decoder network. The third module is purely a geometry-based algorithm to output the final 6D object poses. While the first two modules are learned from data, and the last one not, we believe that this is the best of both worlds: geometry-based and learningbased algorithms for object 6D pose estimation. This is validated by achieving state-of-the-art results for RGB input and a slight improvement over state-of-the-art for RGBD input. However, in contrast to previous work, we achieve these results with the same pipeline for RGB and RGB-D input. Furthermore, to obtain these results, we give a second contribution of a new 3D occlusion-aware and objectcentric data augmentation procedure.
To be or not to be in social media: How brand loyalty is affected by social media?

There is an ongoing debate over the activities of brands and companies in social media. Some researchers believe social media provide a unique opportunity for brands to foster their relationships with customers, while others believe the contrary. Taking the perspective of the brand community building plus the brand trust and loyalty literatures, our goal is to show how brand communities based on social media influence elements of the customer centric model (i.e., the relationships among focal customer and brand, product, company, and other customers) and brand loyalty. A survey-based empirical study with 441 respondents was conducted. The results of structural equation modeling show that brand communities established on social media have positive effects on customer/product, customer/brand, customer/company and customer/other customers relationships, which in turn have positive effects on brand trust, and trust has positive effects on brand loyalty. We find that brand trust has a fully mediating role in converting the effects of enhanced relationships in brand community to brand loyalty. The implications for marketing practice and future research are discussed.
Programmable Logic Controllers

The analysis carried out in this technical paper highlights the concepts, working, advantages and practical applications of programmable logic controllers, along with a comparison with other control systems. A PLC aids in automation of a process by monitoring inputs and controlling outputs after making a decision on the basis of its program. It is commonly used for controlling many mechanical movements of heavy machinery and to control the voltage and frequency of power supplies. PLCs offer an array of advantages over other control systems, and have hence evolved as an important controller in industries these days owing to its large number of applications. General Terms Control Systems
The influence of Pilates training on the ability to contract the Transversus Abdominis muscle in asymptomatic individuals

The neuromuscular system acts to maintain postural stability and reduce the impact of deleterious loads on the spine. Exercising of the abdominal muscles has become widely used in the management of low back pain in order to provide this supplement to spinal stability. Several exercise programmes have been advocated to promote stabilization but evaluation is difficult. This study evaluates two common forms of exercise effects on the ability to appropriately contract Transversus Abdominis (TrA) muscle, whose normal function is regarded as significant in spinal stability. Thirty-six asymptomatic females were examined. Twelve formed the Pilates trained group, 12 the abdominal curl group (both attending a minimum of 25 classes in 6 months) and 12 were non-training controls. A pressure biofeedback unit (PBU) was used to assess performance of the TrA muscle during an abdominal hollowing activity (TrA isolation test) and under limb load (Lumbo-pelvic stability test). The percentage of subjects passing the TrA isolation test was 10 subjects (83%) from the Pilates group, four subjects (33%) from the abdominal curl group, and three subjects (25%) from the control group. The percentage of subjects passing the lumbo-pelvic stability test was five subjects (42%) from the Pilates group, all the subjects from both the abdominal curl and control groups failed the test. The study appears to indicate that Pilates trained subjects could contract the TrA and maintain better lumbo-pelvic control than do those who perform regular abdominal curl exercises, or no abdominal muscle exercises. & 2003 Elsevier Ltd. All rights reserved.
Inhibition of Autophagy is Involved in the Protective Effects of Ginsenoside Rb1 on Spinal Cord Injury

Spinal cord injury (SCI) is a devastating neurological disorder. Autophagy is induced and plays a crucial role in SCI. Ginsenoside Rb1 (Rb1), one of the major active components extracted from Panax Ginseng CA Meyer, has exhibited neuroprotective effects in various neurodegenerative diseases. However, it remains unknown whether autophagy is involved in the neuroprotection of Rb1 on SCI. In this study, we examined the regulation of autophagy following Rb1 treatment and its involvement in the Rb1-induced neuroprotection in SCI and in vitro injury model. Firstly, we found that Rb1 treatment decreased the loss of motor neurons and promoted function recovery in the SCI model. Furthermore, we found that Rb1 treatment inhibited autophagy in neurons, and suppressed neuronal apoptosis and autophagic cell death in the SCI model. Finally, in the in vitro injury model, Rb1 treatment increased the viability of PC12 cells and suppressed apoptosis by inhibiting excessive autophagy, whereas stimulation of autophagy by rapamycin abolished the anti-apoptosis effect of Rb1. Taken together, these findings suggest that the inhibition of autophagy is involved in the neuroprotective effects of Rb1 on SCI.
The Ties That Bind : Measuring the Strength of Consumers ’ Emotional Attachments to BrandsTHE

Extant research suggests that consumers can become emotionally attached to consumption objects, including brands. However, a scale to measure the strength of consumers’ emotional attachments to brands has yet to be devised. We develop such a scale in Studies 1 and 2. Study 3 validates the scale’s internal consistency and dimensional structure. Study 4 examines its convergent validity with respect to four behavioral indicators of attachments. Study 5 demonstrates discriminant validity, showing that the scale is differentiated from measures of satisfaction, involvement, and brand attitudes. That study also examines the scale’s predictive validity, showing that it is positively associated with indicators of both commitment and investment. The limitations of the scale and the boundary conditions of its applicability are also discussed.
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (eg. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Furthermore, deep learning research argues  that learning representation with over-complete model are more prone to overfitting compared to the approach that learns from noisy data. Thus, we investigate a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion. It outperforms prior work on recognition as well as more challenging tasks like denoising and shape completion. In addition, our approach is atleast two order of magnitude faster at test time and thus, provides a path to scaling up 3D deep learning.
Exchangeability and Kernel Invariance in Trained MLPs

In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as SGD. A relaxation of the IID condition is a probabilistic symmetry known as exchangeability. We show the sense in which the weights in MLPs are exchangeable. This yields the result that in certain instances, the layer-wise kernel of fully-connected layers remains approximately constant during training. We identify a sharp change in the macroscopic behavior of networks as the covariance between weights changes from zero.
Epidemic prediction and control in weighted networks.

Contact networks are often used in epidemiological studies to describe the patterns of interactions within a population. Often, such networks merely indicate which individuals interact, without giving any indication of the strength or intensity of interactions. Here, we use weighted networks, in which every connection has an associated weight, to explore the influence of heterogeneous contact strengths on the effectiveness of control measures. We show that, by using contact weights to evaluate an individual's influence on an epidemic, individual infection risk can be estimated and targeted interventions such as preventative vaccination can be applied effectively. We use a diary study of social mixing behaviour to indicate the patterns of contact weights displayed by a real population in a range of different contexts, including physical interactions; we use these data to show that considerations of link weight can in some cases lead to improved interventions in the case of infections that spread through close contact interactions. However, we also see that simpler measures, such as an individual's total number of social contacts or even just their number of contacts during a single day, can lead to great improvements on random vaccination. We therefore conclude that, for many infections, enhanced social contact data can be simply used to improve disease control but that it is not necessary to have full social mixing information in order to enhance interventions.