nils-herrmann/clean_scirepeval huggingface dataset:
PixelSNAIL: An Improved Autoregressive Generative Model

Autoregressive generative models achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this paper, we describe the resulting model and present state-of-the-art log-likelihood results on heavily benchmarked datasets: CIFAR-10 (2.85 bits per dim), 32× 32 ImageNet (3.80 bits per dim) and 64 × 64 ImageNet (3.52 bits per dim). Our implementation will be made available at anonymized.
Deep GrabCut for Object Selection

Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.
Mind your ( R , Φ ) s : Location-Based Privacy Controls for Consumer Drones

This position paper explores the threat to individual privacy due to the widespread use of consumer drones. Present day consumer drones are equipped with sensors such as cameras and microphones, and their types and numbers can be well expected to increase in future. Drone operators have absolute control on where the drones fly and what the on-board sensors record with no options for bystanders to protect their privacy. This position paper proposes a policy language that allows homeowners, businesses, governments, and privacy-conscious individuals to specify location access-control for drones, and discusses how these policy-based controls might be realized in practice. This position paper also explores the potential future problem of managing consumer drone traffic that is likely to emerge with increasing use of consumer drones for various tasks. It proposes a privacy preserving traffic management protocol for directing drones towards their respective destinations without requiring drones to reveal their destinations.
A Comparative Survey of DBpedia , Freebase , OpenCyc , Wikidata , and YAGO

In recent years, several noteworthy large, crossdomain and openly available knowledge graphs (KGs) have been created. These include DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Although extensively in use, these KGs have not been subject to an in-depth comparison so far. In this survey, we first define aspects according to which KGs can be analyzed. Next, we analyze and compare the above mentioned KGs along those aspects and finally propose a method for finding the most suitable KG for a given setting.
E-government Diffusion: A Comparison of Adoption Constructs

E-government adoption is the focus of many research studies. However, few studies have compared the adoption factors to identify the most salient predictors of e-government use. This study compares popular adoption constructs to identify the most influential. A survey was administered to elicit citizen perceptions of e-government services. The results of stepwise regression indicate perceived usefulness, trust of the internet, previous use of an e-government service and perceived ease of use all have a significant impact on one’s intention to use an e-government service. The implications for research and practice are discussed below.
Credit Card Customer Segmentation and Target Marketing Based on Data Mining

Based on the real data of a Chinese commercial bank’s credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit cards holders. Conclusively, we obtain some useful information of decision tree regulation by the best model among the four. The information is not only helpful for the bank to understand related characteristics of different customers, but also marketing representatives to find potential customers and to implement target marketing.
*SEM 2012 Shared Task: Resolving the Scope and Focus of Negation

The Joint Conference on Lexical and Computational Semantics (*SEM) each year hosts a shared task on semantic related topics. In its first edition held in 2012, the shared task was dedicated to resolving the scope and focus of negation. This paper presents the specifications, datasets and evaluation criteria of the task. An overview of participating systems is provided and their results are summarized.
The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer

A common task in image editing is to change the colours of a picture to match the desired colour grade of another picture. Finding the correct colour mapping is tricky because it involves numerous interrelated operations, like balancing the colours, mixing the colour channels or adjusting the contrast. Recently, a number of automated tools have been proposed to find an adequate one-to-one colour mapping. The focus in this paper is on finding the best linear colour transformation. Linear transformations have been proposed in the literature but independently. The aim of this paper is thus to establish a common mathematical background to all these methods. Also, this paper proposes a novel transformation, which is derived from the Monge-Kantorovicth theory of mass transportation. The proposed solution is optimal in the sense that it minimises the amount of changes in the picture colours. It favourably compares theoretically and experimentally with other techniques for various images and under various colour spaces.
Building trust in m-commerce: contributions from quality and satisfaction

Mobile commerce (m-commerce) represents a new area of business opportunity. Past research has often focused on customer acceptance and its antecedents, rather than factors that build trust in m-commerce. The purpose of this paper is to provide an explanation of factors influencing customer trust towards vendors on the mobile internet.  M-commerce relies on mobile technology and well-maintained service quality. This paper has applied the service quality model (SERVQUAL) and technology acceptance model (TAM), coupled with proposed quality factors in relation to m-commerce that, according to the literature, influence customer trust, to empirically test the formation of trust. The proposed model was empirically evaluated using online survey data from 212 experienced m-commerce customers.  The results showed that despite customisation, brand image and satisfaction all directly affecting customer trust towards the vendor in m-commerce, customisation and brand image equally had a stronger direct effect on trust formation. In addition, interactivity and responsiveness had no direct impact, but had an indirect impact via satisfaction on trust towards the vendor.  This paper contributes a theoretical understanding of factors that activate the development of trust towards the vendor. For vendors in general the results enable them to better develop customer trust in m-commerce.  The paper verifies the effects of satisfaction and proposed quality factors on customer confidence in m-commerce. Moreover, this article validates the determinants of satisfaction.
Wideband Circularly Polarized Dielectric Bird-Nest Antenna With Conical Radiation Pattern

A wideband circularly polarized (CP) antenna with a conical radiation pattern is investigated. It consists of a feeding probe and parasitic dielectric parallelepiped elements that surround the probe. Since the structure of the antenna looks like a bird nest, it is named as bird-nest antenna. The probe, which protrudes from a circular ground plane, operates in its fundamental monopole mode that generates omnidirectional linearly polarized (LP) fields. The dielectric parallelepipeds constitute a wave polarizer that converts omnidirectional LP fields of the probe into omnidirectional CP fields. To verify the design, a prototype operating in C band was fabricated and measured. The reflection coefficient, axial ratio (AR), radiation pattern, and antenna gain are studied, and reasonable agreement between the measured and simulated results is observed. The prototype has a 10-dB impedance bandwidth of 41.0% and a 3-dB AR bandwidth of as wide as 54.9%. A parametric study was carried out to characterize the proposed antenna. Also, a design guideline is given to facilitate designs of the antenna.
Tourism Demand Modelling and Forecasting A Review of Recent Research

This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there is no single model that consistently outperforms other models in all situations. Furthermore, this study identifies some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events’ impact assessment and risk forecasting.
Multi-Dimensional Dynamic Time Warping for Gesture Recognition

We present an algorithm for Dynamic Time Warping (DTW) on multi-dimensional time series (MDDTW). The algorithm utilises all dimensions to find the best synchronisation. It is compared to ordinary DTW, where a single dimension is used for aligning the series. Both one-dimensional and multidimensional DTW are also tested when derivatives instead of feature values are used for calculating the warp. MD-DTW performed best in finding a known ground truth under noisy conditions. The algorithms were also used to perform simple classification of a set of 121 gestures. MD-DTW performed as well as or better than any single dimension in all tasks. In general, DTW on feature derivatives gave better results than DTW on feature values.
Automated Markerless Analysis of Human Gait Motion for Recognition and Classification

Yoo and Mark S. Nixon 259 We present a new method for an automated markerless system to describe, analyze, and classify human gait motion. The automated system consists of three stages: i) detection and extraction of the moving human body and its contour from image sequences, ii) extraction of gait figures by the joint angles and body points, and iii) analysis of motion parameters and feature extraction for classifying human gait. A sequential set of 2D stick figures is used to represent the human gait motion, and the features based on motion parameters are determined from the sequence of extracted gait figures. Then, a knearest neighbor classifier is used to classify the gait patterns. In experiments, this provides an alternative estimate of biomechanical parameters on a large population of subjects, suggesting that the estimate of variance by marker-based techniques appeared generous. This is a very effective and well-defined representation method for analyzing the gait motion. As such, the markerless approach confirms uniqueness of the gait as earlier studies and encourages further development along these lines.
Exploring the Potential of Gamification Among Frail Elderly Persons

The application of game elements a in non-gaming context offers a great potential regarding the engagement of senior citizens with information systems. In this paper, we suggest the application of gamification to routine tasks and leisure activities, namely physical and cognitive therapy, the gamification of real-life activities which are no longer accessible due to age-related changes and the application of game design elements to foster social interaction. Furthermore, we point out important chances and challenges such as the lack of gaming experience among the target audience and highlight possible areas for future work which offer valuable design opportunities for frail elderly audiences.
Unsupervised Learning from Video to Detect Foreground Objects in Single Images

Unsupervised learning from visual data is one of the most difficult challenges in computer vision. It is essential for understanding how visual recognition works. Learning from unsupervised input has an immense practical value, as huge quantities of unlabeled videos can be collected at low cost. Here we address the task of unsupervised learning to detect and segment foreground objects in single images. We achieve our goal by training a student pathway, consisting of a deep neural network that learns to predict, from a single input image, the output of a teacher pathway that performs unsupervised object discovery in video. Our approach is different from the published methods that perform unsupervised discovery in videos or in collections of images at test time. We move the unsupervised discovery phase during the training stage, while at test time we apply the standard feed-forward processing along the student pathway. This has a dual benefit: firstly, it allows, in principle, unlimited generalization possibilities during training, while remaining fast at testing. Secondly, the student not only becomes able to detect in single images significantly better than its unsupervised video discovery teacher, but it also achieves state of the art results on two current benchmarks, YouTube Objects and Object Discovery datasets. At test time, our system is two orders of magnitude faster than other previous methods.
JRC-Names: Multilingual entity name variants and titles as Linked Data

Since 2004 the European Commission’s Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Беньямин/نیماینب Netanyahu/Netanjahu/Nétanyahou/Netahny/Нетаньяху/وهاینتن). This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union’s Open Data Portal.
Esthetic potential of single-implant provisional restorations: selection criteria of available alternatives.

Provisional implant restorations are essential tools used to optimize the esthetic outcomes for single-tooth implant restorations. This article describes three groups of available alternatives and the specific options within each group including removable prostheses (interim removable partial denture, vacuum-formed appliance), tooth-supported prostheses (bonded extracted or denture teeth, cast metal or fiber-reinforced resin-bonded fixed partial denture [FPD], wire-retained resin-bonded FPD, acrylic resin provisional FPD), and implant-supported fixed prostheses (implant-retained provisional restoration). Advantages and disadvantages as well as fabrication guidelines for each option are provided. The author proposes that the restorative dentist consider eight criteria in selecting the most appropriate type of provisional prosthesis for a specific patient situation including the esthetic potential, patient comfort, treatment time, laboratory cost, occlusal clearance, ease of removal, durability, and ease of modification. The patient's esthetic expectations are critical in determining the most suitable type of provisional restoration. Esthetically pleasing provisional restorations are part of the evolving implant continuum, making implant dentistry more appealing to practicing dentists and potential patients.



Provisional restorations for single implants have evolved from temporary expedients during osseous and soft tissue integration to critical therapeutic tools used to assess patient expectations, communicate with the laboratory, and optimize definitive implant treatment. The selection of the type of provisional restoration may significantly influence esthetics during the period of implant integration and soft tissue healing. However, it is unlikely that there is a direct correlation between the type of provisional restoration used and the esthetic outcome of the definitive prosthesis.
STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition

In this paper, we present a novel SpaTial Attention Residue Network (STAR-Net) for recognising scene texts. The overall architecture of our STAR-Net is illustrated in fig. 1. Our STARNet emphasises the importance of representative image-based feature extraction from text regions by the spatial attention mechanism and the residue learning strategy. It is by far the deepest neural network proposed for scene text recognition.
Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network

This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions. Our method introduces accompanying hierarchical-nested adversarial objectives inside the network hierarchies, which regularize mid-level representations and assist generator training to capture the complex image statistics. We present an extensile single-stream generator architecture to better adapt the jointed discriminators and push generated images up to high resolutions. We adopt a multi-purpose adversarial loss to encourage more effective image and text information usage in order to improve the semantic consistency and image fidelity simultaneously. Furthermore, we introduce a new visual-semantic similarity measure to evaluate the semantic consistency of generated images. With extensive experimental validation on three public datasets, our method significantly improves previous state of the arts on all datasets over different evaluation metrics.
Tweeting propaganda, radicalization and recruitment: Islamic state supporters multi-sided twitter networks

Islamic State (IS) terrorist networks in Syria and Iraq pose threats to national security. IS' exploitation of social media and digital strategy plays a key role in its global dissemination of propaganda, radicalization, and recruitment. However, systematic research on Islamic terrorist communication via social media is limited. Our research investigates the question: How do IS members/supporters use Twitter for terrorism communication: propaganda, radicalization, and recruitment? Theoretically, we drew on microeconomic network theories to develop a theoretical framework for multi-sided Twitter networks in the global Islamic terrorist communication environment. Empirically, we collected 3,039 tweets posted by @shamiwitness who was identified in prior research as "an information disseminator" for the IS cause. Methodologically, we performed social network analysis, trend and content analyses of the tweet data. We find strong evidence for Shamiwitness-intermediated multi-sided Twitter networks of international mass media, regional Arabic mass media, IS fighters, and IS sympathizers, supporting the framework's utility.
Learning representations of emotional speech with deep convolutional generative adversarial networks

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often “overshadowed” by voice characteristics relating to emotional intensity or emotional activation. In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech. In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning. Within our extensive experiments we leverage a multitask annotated emotional corpus as well as a large unlabeled meeting corpus (around 100 hours). Our speaker-independent classification experiments show that in particular the use of unlabeled data in our investigations improves performance of the classifiers and both fully supervised baseline approaches are outperformed considerably. We improve the classification of emotional valence on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which is competitive to state-of-the-art performance.
Annotating and Predicting Non-Restrictive Noun Phrase Modifications

The distinction between restrictive and non-restrictive modification in noun phrases is a well studied subject in linguistics. Automatically identifying non-restrictive modifiers can provide NLP applications with shorter, more salient arguments, which were found beneficial by several recent works. While previous work showed that restrictiveness can be annotated with high agreement, no large scale corpus was created, hindering the development of suitable classification algorithms. In this work we devise a novel crowdsourcing annotation methodology, and an accompanying large scale corpus. Then, we present a robust automated system which identifies non-restrictive modifiers, notably improving over prior methods.