nils-herrmann/clean_scirepeval huggingface dataset:
DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks

The online car-hailing service has gained great popularity all over the world. As more passengers and more drivers use the service, it becomes increasingly more important for the the car-hailing service providers to effectively schedule the drivers to minimize the waiting time of passengers and maximize the driver utilization, thus to improve the overall user experience. In this paper, we study the problem of predicting the real-time car-hailing supply-demand, which is one of the most important component of an effective scheduling system. Our objective is to predict the gap between the car-hailing supply and demand in a certain area in the next few minutes. Based on the prediction, we can balance the supply-demands by scheduling the drivers in advance. We present an end-to-end framework called Deep Supply-Demand (DeepSD) using a novel deep neural network structure. Our approach can automatically discover complicated supply-demand patterns from the car-hailing service data while only requires a minimal amount hand-crafted features. Moreover, our framework is highly flexible and extendable. Based on our framework, it is very easy to utilize multiple data sources (e.g., car-hailing orders, weather and traffic data) to achieve a high accuracy. We conduct extensive experimental evaluations, which show that our framework provides more accurate prediction results than the existing methods.
ObamaNet: Photo-realistic lip-sync from text

We present ObamaNet, the first architecture that takes any text as input and generates both the corresponding speech and synchronized photo-realistic lip-sync videos. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisely, we use three main modules: a text-to-speech network based on Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio, and a network based on Pix2Pix to generate the video frames conditioned on the keypoints.
Comparison of visual vs. automated detection of lipemic, icteric and hemolyzed specimens: can we rely on a human eye?

Results from hemolyzed, icteric, and lipemic samples may be inaccurate and can lead to medical errors. These preanalytical interferences may be detected using visual or automated assessment. Visual inspection is time consuming, highly subjective and not standardized. Our aim was to assess the comparability of automated spectrophotometric detection and visual inspection of lipemic, icteric and hemolyzed samples.



This study was performed on 1727 routine biochemistry serum samples. Automated detection was performed using the Olympus AU2700 analyzer. We assessed: 1) comparability of visual and automated detection of lipemic, icteric and hemolyzed samples, 2) precision of automated detection, and 3) inter-observer variability for visual inspection.



Weighted kappa coefficients for comparability of visual and automated detection were: 0.555, 0.529 and 0.638, for lipemic, icteric and hemolyzed samples, respectively. The precision for automated detection was high for all interferences, with the exception of samples being only slightly lipemic. The best overall agreement between observers was present in assessing lipemia (mean weighted kappa=0.698), whereas the lowest degree of agreement was observed in assessing icterus (mean weighted kappa=0.476).



Visual inspection of lipemic, icteric and hemolyzed samples is highly unreliable and should be replaced by automated systems that report serum indices.
Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation

Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.
A new IoT combined face detection of people by using computer vision for security application

In nowadays situation, the security forms the most important section of our lives. Security of the house or the near and dear ones is important to everybody. Home automation is an exciting area for security applications. This field has enhanced with new technologies such Internet of things (IoT). In IoT, every gadget behaves as a little part of an internet node and every node interact and communicate. Lately, security cameras are utilized in order to build safety places, homes, and cities. However, this technology needs a person who detects any problem in the frame taken from the camera. In this paper, an Internet of Things is joined with computer vision in order to detect the faces of people. For this purpose, to execute this system, a credit card size computer that utilizes its own camera board for the security system, such as raspberry pi 3 utilized. Likewise, Passive Infrared Sensor (PIR) mounted on the Raspberry PI is utilized to detect any movements. So it helps to monitor and get notifications when motion is identified, captures the image and detect the faces, then sends images to a Smartphone via utilizing telegram application. Internet of things based on telegram application used to see the activity and get notices when movement is detected.
Towards Open-Set Identity Preserving Face Synthesis

We propose a framework based on Generative Adversarial Networks to disentangle the identity and attributes of faces, such that we can conveniently recombine different identities and attributes for identity preserving face synthesis in open domains. Previous identity preserving face synthesis processes are largely confined to synthesizing faces with known identities that are already in the training dataset. To synthesize a face with identity outside the training dataset, our framework requires one input image of that subject to produce an identity vector, and any other input face image to extract an attribute vector capturing, e.g., pose, emotion, illumination, and even the background. We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute. Our proposed framework does not need to annotate the attributes of faces in any way. It is trained with an asymmetric loss function to better preserve the identity and stabilize the training process. It can also effectively leverage large amounts of unlabeled training face images to further improve the fidelity of the synthesized faces for subjects that are not presented in the labeled training face dataset. Our experiments demonstrate the efficacy of the proposed framework. We also present its usage in a much broader set of applications including face frontalization, face attribute morphing, and face adversarial example detection.
Correlation Pattern Recognition

Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis, and use of correlation pattern recognition algorithms require background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing, and optical processing. This book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains state-of-the-art technology presented by the team that developed it and includes case studies of significant current interest, such as face and target recognition. It is suitable for advanced undergraduate or graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.
Folktale conceptual model based on folktale classification system of type, motif, and function

This paper elaborates a study conducted to propose a folktale conceptual model based on folktale classification systems of type, motif, and function. Globally, three distinguish folktale classification systems exist and have been used for many years nonetheless not actually converge and achieve agreement on classification issues. The study aims to develop a conceptual model that visually depicts the combination and connection of the three folktale classification systems. The method opted for the conceptual model development is pictorial representation. It is hoped that the conceptual model developed would be an early platform to subsequently catalyze more robust and cohesive folktale classification system.
Improving the Resolution of CNN Feature Maps Efficiently with Multisampling

We describe a new class of subsampling techniques for CNNs, termed multisampling, that significantly increases the amount of information kept by feature maps through subsampling layers. One version of our method, which we call checkered subsampling, significantly improves the accuracy of state-of-the-art architectures such as DenseNet and ResNet without any additional parameters and, remarkably, improves the accuracy of certain pretrained ImageNet models without any training or fine-tuning. We glean new insight into the nature of data augmentations and demonstrate, for the first time, that coarse feature maps are significantly bottlenecking the performance of neural networks in image classification.
Electron and Phonon Properties of Graphene : Their Relationship with Carbon Nanotubes

The discovery of Novoselov et al. (2004) of a simple method to transfer a single atomic layer of carbon from the c-face of graphite to a substrate suitable for the measurement of its electrical and optical properties has led to a renewed interest in what was considered to be before that time a prototypical, yet theoretical, two-dimensional system. Indeed, recent theoretical studies of graphene reveal that the linear electronic band dispersion near the Brillouin zone corners gives rise to electrons and holes that propagate as if they were massless fermions and anomalous quantum transport was experimentally observed. Recent calculations and experimental determination of the optical phonons of graphene reveal Kohn anomalies at high-symmetry points in the Brillouin zone. They also show that the Born– Oppenheimer principle breaks down for doped graphene. Since a carbon nanotube can be viewed as a rolled-up sheet of graphene, these recent theoretical and experimental results on graphene should be important to researchers working on carbon nanotubes. The goal of this contribution is to review the exciting news about the electronic and phonon states of graphene and to suggest how these discoveries help understand the properties of carbon nanotubes.
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music

Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind’s WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.
Time Domain Flutter and Buffeting Response Analysis of Bridges Fig. 1. Aerodynamic Forces on Bridge Section

A time domain approach for predicting the coupled flutter and buffeting response of long span bridges is presented. The frequency dependent unsteady aerodynamic forces are represented by the convolution integrals involving the aerodynamic impulse function and structural motions or wind fluctuations. The aerodynamic impulse functions are derived from experimentally measured flutter derivatives, aerodynamic admittance functions, and spanwise coherence of aerodynamic forces using rational function approximations. A significant feature of the approach presented here is that the frequency dependent characteristics of unsteady aerodynamic forces and the nonlinearities of both aerodynamic and structural origins can be modeled in the response analysis. The flutter and buffeting response of a long span suspension bridge is analyzed using the proposed time domain approach. The results show good agreement with those from the frequency domain analysis. The example used to demonstrate the proposed scheme focuses on the treatment of frequency dependent self-excited and buffeting force effects. Application to nonlinear effects will be addressed in a future publication.
Generating brand awareness in Online Social Networks

With their exceptional interactive and communicative capabilities, Online Social Networks (OSNs) allow destinations and companies to heighten their brand awareness. Many tourist destinations and hospitality brands are exploring the use of OSNs to form brand awareness and generate positive WOM. The purpose of this research is to propose and empirically test a theory-driven model of brand awareness in OSNs. A survey among 230 OSN users was deployed to test the theoretical model. The data was analyzed using SEM. Study results indicate that building brand awareness in OSNs increases WOM traffic. In order to foster brand awareness in OSN, it is important to create a virtually interactive environment, enabling users to exchange reliable, rich and updated information in a timely manner. Receiving financial and/or psychological rewards and accessing exclusive privileges in OSNs are important factors for users. Both system quality and information quality were found to be important precursors of brand awareness in OSNs. Study results support the importance of social media in online branding strategies. Virtual interactivity, system quality, information content quality, and rewarding activities influence and generate brand awareness, which in return, triggers WOM.
A Classification Approach for Predicting Plant Leaf Diseases in Digital Image Processing

In agriculture, detection and diagnosis of plant disease using digital image processing techniques focused on accurate segmentation of healthy and diseased tissue. Among various segmentation methods, the most widely used semiautomatic segmentation is based on gray scale histogram. In a novel semi-automatic segmentation process, the edges were removed along with pixels and then color conversion was done. After color conversion, pixel value adjustments and contrast enhancement of an image were performed to improve the image quality. Histogram with 100 bins was constructed for recognizing the diseased tissue from the healthier part of a leaf image. At last, segmentation of diseased leaf was found based on the histogram bins. Such bins were found manually which is not easy for all cases. Moreover, detection accuracy was reduced the quality by the influence of reflection light and distortion regions in an acquired image. Hence reflection light and distortion from image were removed using Quality Assessment Method Scheme (QAMS) algorithm. For automatic separation of diseased part from the healthier regions in a leaf image an optimization algorithm is required. To automatically define the histogram bins and separate diseased part from the healthier regions in a leaf image the Convolutional Neural Networks (CNN) algorithm used. After segmenting the diseased leaf image, the classification is done by Support Vector Machine (SVM) to detect the leaf diseases. The method provides better detection accuracy and computational time is reduced. CNN, Diseases detection, Distortion removal, Reflection light, SVM.
An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification

This paper presents a survey as well as an empirical comparison and evaluation of seven kernels on graphs and two related similarity matrices, that we globally refer to as "kernels on graphs" for simplicity. They are the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regularized Laplacian kernel, the commute-time (or resistance-distance) kernel, the random-walk-with-restart similarity matrix, and finally, a kernel first introduced in this paper (the regularized commute-time kernel) and two kernels defined in some of our previous work and further investigated in this paper (the Markov diffusion kernel and the relative-entropy diffusion matrix). The kernel-on-graphs approach is simple and intuitive. It is illustrated by applying the nine kernels to a collaborative-recommendation task, viewed as a link prediction problem, and to a semisupervised classification task, both on several databases. The methods compute proximity measures between nodes that help study the structure of the graph. Our comparisons suggest that the regularized commute-time and the Markov diffusion kernels perform best on the investigated tasks, closely followed by the regularized Laplacian kernel.
TVQA: Localized, Compositional Video Question Answering

Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a largescale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available at http://tvqa.cs.unc.edu.
Identifying Partisan Slant in News Articles and Twitter during Political Crises

In this paper, we are interested in understanding the interrelationships between mainstream and social media in forming public opinion during mass crises, specifically in regards to how events are framed in the mainstream news and on social networks and to how the language used in those frames may allow to infer political slant and partisanship. We study the lingual choices for political agenda setting in mainstream and social media by analyzing a dataset of more than 40M tweets and more than 4M news articles from the mass protests in Ukraine during 2013-2014 — known as "Euromaidan" — and the post-Euromaidan conflict between Russian, pro-Russian and Ukrainian forces in eastern Ukraine and Crimea. We design a natural language processing algorithm to analyze at scale the linguistic markers which point to a particular political leaning in online media and show that political slant in news articles and Twitter posts can be inferred with a high level of accuracy. These findings allow us to better understand the dynamics of partisan opinion formation during mass crises and the interplay between mainstream and social media in such circumstances.
Bidirectional converter for vehicle to grid (V2G) reactive power operation

In this paper, a bidirectional converter with a uniform controller for Vehicle to grid (V2G) application is designed. The bidirectional converter consists of two stages one is ac-dc converter and second is dc-dc converter. For ac-dc converter bipolar modulation is used. Two separate controller systems are designed for converters which follow active and reactive power commands from grid. Uniform controller provides reactive power support to the grid. The charger operates in two quadrants I and IV. There are three modes of operation viz. charging only operation, charging-capacitive operation and charging-inductive operation. During operation under these three operating modes vehicle's battery is not affected. The whole system is tested using MATLAB/SIMULINK.
High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Traffic forecasting is a challenging task, due to the complicated spatial dependencies on roadway networks and the time-varying traffic patterns. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, High-Order Graph Convolutional Long Short-Term Memory Neural Network (HGC-LSTM), to learn the interactions between links in the traffic network and forecast the network-wide traffic state. We define the high-order traffic graph convolution based on the physical network topology. The proposed framework employs L1-norms on the graph convolution weights and L2-norms on the graph convolution features to identify the most influential links in the traffic network. We propose a novel Real-Time Branching Learning (RTBL) algorithm for the HGC-LSTM framework to accelerate the training process for spatio-temporal data. Experiments show that our HGC-LSTM network is able to capture the complex spatio-temporal dependencies efficiently present in the traffic network and consistently outperforms state-of-the-art baseline methods on two heterogeneous real-world traffic datasets. The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks.