nils-herrmann/clean_scirepeval huggingface dataset:
Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset

In modern cities, more and more vehicles, such as taxis, have been equipped with GPS devices for localization and navigation. Gathering and analyzing these large-scale real-world digital traces have provided us an unprecedented opportunity to understand the city dynamics and reveal the hidden social and economic “realities”. One innovative pervasive application is to provide correct driving strategies to taxi drivers according to time and location. In this paper, we aim to discover both efficient and inefficient passenger-finding strategies from a large-scale taxi GPS dataset, which was collected from 5350 taxis for one year in a large city of China. By representing the passenger-finding strategies in a Time-Location-Strategy feature triplet and constructing a train/test dataset containing both top- and ordinary-performance taxi features, we adopt a powerful feature selection tool, L1-Norm SVM, to select the most salient feature patterns determining the taxi performance. We find that the selected patterns can well interpret the empirical study results derived from raw data analysis and even reveal interesting hidden “facts”. Moreover, the taxi performance predictor built on the selected features can achieve a prediction accuracy of 85.3% on a new test dataset, and it also outperforms the one based on all the features, which implies that the selected features are indeed the right indicators of the passenger-finding strategies.
Segmentation of Dermoscopy Images Using Wavelet Networks

This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.
Table of Contents Recognition and Extraction for Heterogeneous Book Documents

Existing work on book table of contents (TOC) recognition has been almost all on small size, application-dependent, and domain-specific datasets. However, TOC of books from different domains differ significantly in their visual layout and style, making TOC recognition a challenging problem for a large scale collection of heterogeneous books. We observed that TOCs can be placed into three basic styles, namely "flat", "ordered", and "divided", giving insights into how to achieve effective TOC parsing. As such, we propose a new TOC recognition approach which adaptively decides the most appropriate TOC parsing rules based on the classification of these three TOC styles. Evaluation on large number, over 25,000, of book documents from various domains demonstrates its effectiveness and efficiency.
Symmetry and sexual dimorphism in human faces: interrelated preferences suggest both signal quality

Symmetry and masculinity in human faces have been proposed to be cues to the quality of the owner. Accordingly, symmetry is generally found attractive in male and female faces and femininity is attractive in female faces. Women’s preferences for male facial masculinity vary in ways that may maximise genetic benefits to women’s offspring. Here we examine sameand opposite-sex preferences for both traits (Study 1) and intercorrelations between preferences for symmetry and sexual dimorphism in faces (Study 1, Study 2) using computer manipulated faces. For symmetry, we found that male and female judges preferred symmetric faces more when judging faces of the opposite-sex than when judging same-sex faces. A similar pattern was seen for sexual dimorphism (i.e. women preferred more masculine male faces than men did), but women also showed stronger preferences for femininity in female faces than men reported. This suggests that women are more concerned with female femininity than are men. We also found that in women preferences for symmetry were positively correlated with preferences for masculinity in male faces and that in men preferences for symmetry were positively correlated with preferences for femininity in female faces. These latter findings suggest that symmetry and sexual dimorphism advertise a common quality in faces or that preferences for these facial cues are dependent on a common quality in the judges. Collectively, our findings support the view that preferences for symmetry and sexual dimorphism are related to mechanisms involved in sexual selection and mate choice rather than functionless by-products of other perceptual mechanisms.
Applications of Data Mining Techniques in TelecomChurn Prediction

In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.
Decentralized frequency control of a DDG-PV Microgrid in islanded mode

This paper presents an innovative approach to control the frequency in Diesel-Driven Generator (DDG)-Photovoltaic (PV) Microgrid (MG) in islanded mode. The common approach is based on hierarchical control: primary control and secondary control. The conventional primary controller is a P-controller. Compared to this, we propose a novel primary controller for PV to mimic the dynamic behavior of the conventional synchronous generator (SG) so that more inertia in the MG can be obtained. The proposed secondary control is accomplished decentralized, too, so that no communication links for the real-time control are required and the single point of failure can be avoided. Although in this approach every controller works locally, the controllers and their parameters are designed centrally. This means, the controllers can be appropriately adjusted, when the parameters or the topology of the system change. The parameters of the controllers are optimized by minimizing the H-inf-norm using a known non-smooth method.
Combining Social Network Analysis with Semi-supervised Clustering : a case study on fraud detection

At time of crisis, when fraud permanently frightens the basis of modern societies, the existence of effective tools to prevent it, or just to identify it in time, is critical. However, the detection of fraud is naturally impaired (among other issues) by the difficulty on labelling data, due to the cost of identifying and attest fraud. Moreover, the inability to incorporate domain knowledge in the mining process makes classifiers to use inadequate attributes to distinguish entities, ignoring most of existing relevant information, like entities’ social relations. In this paper, we propose a new methodology for enriching semi-supervised clustering with information collected through the analysis of social networks. The methodology is then applied on tax fraud detection and assessed by measuring the impact of this enrichment in the accuracy of semi-supervised clustering methods.
Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

In this letter, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.
Fast Incremental and Personalized PageRank

In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter. For global PageRank, we assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order. We show that with a reset probability of ǫ, the total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank of every node at all times is O( lnm ǫ ). This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs Ω( m 2
Bundled camera paths for video stabilization

We present a novel video stabilization method which models camera motion with a bundle of (multiple) camera paths. The proposed model is based on a mesh-based, spatially-variant motion representation and an adaptive, space-time path optimization. Our motion representation allows us to fundamentally handle parallax and rolling shutter effects while it does not require long feature trajectories or sparse 3D reconstruction. We introduce the 'as-similar-as-possible' idea to make motion estimation more robust. Our space-time path smoothing adaptively adjusts smoothness strength by considering discontinuities, cropping size and geometrical distortion in a unified optimization framework. The evaluation on a large variety of consumer videos demonstrates the merits of our method.
DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.
Industrial Collaborative Robot Design: a Guideline for Future Design Activity

This study explores the rapidly expanding Collaborative Robot market and the recent literature in social robotics. An attempt is made to interpret both resources to define macro-parameter trends that could be useful guidelines for robot designing. The market shows a trend towards anthropomorphism in collaborative robot design, though some studies suggest that functionality of robots (not sociality) should always be explicit and evident in their design. An unknown area of convergence between these two trends is hypothesised; here hides the most anthropomorphic design accepted by human collaborators.
Quality Management in Systems Development: An Organizational System Perspective

We identify top management leadership, a sophisticated management infrastructure, process management efficacy, and stakeholder participation as important elements of a quality-oriented organizational system for software development. A model interrelating these constructs and quality performance is proposed. Data collected through a national survey of IS executives in Fortune 1000 companies and government agencies was used to Robert Zmud was the accepting senior editor for this paper. test the model using a Partial Least Squares analysis methodology. Our results suggest that software quality goals are best attained when top management creates a management infrastructure that promotes improvements in process design and encourages stakeholders to evolve the design of the development processes. Our results also suggest that all elements of the organizational system need to be developed in order to attain quality goals and that piecemeal adoption of select quality management practices are unlikely to be effective. Implications of this research for IS theory and practice are discussed.
A Semi-supervised Framework for Image Captioning

State-of-the-art approaches for image captioning require supervised training data consisting of captions with paired image data. These methods are typically unable to use unsupervised data such as textual data with no corresponding images, which is a much more abundant commodity. We here propose a novel way of using such textual data by artificially generating missing visual information. We evaluate this learning approach on a newly designed model that detects visual concepts present in an image and feed them to a reviewer-decoder architecture with an attention mechanism. Unlike previous approaches that encode visual concepts using word embeddings, we instead suggest using regional image features which capture more intrinsic information. The main benefit of this architecture is that it synthesizes meaningful thought vectors that capture salient image properties and then applies a soft attentive decoder to decode the thought vectors and generate image captions. We evaluate our model on both Microsoft COCO and Flickr30K datasets and demonstrate that this model combined with our semi-supervised learning method can largely improve performance and help the model to generate more accurate and diverse captions.
Shadow Detection with Conditional Generative Adversarial Networks

We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwise potentials in a Conditional Random Field. In contrast, the proposed adversarial approach is able to model higher level relationships and global scene characteristics. We train a shadow detector that corresponds to the generator of a conditional GAN, and augment its shadow accuracy by combining the typical GAN loss with a data loss term. Due to the unbalanced distribution of the shadow labels, we use weighted cross entropy. With the standard GAN architecture, properly setting the weight for the cross entropy would require training multiple GANs, a computationally expensive grid procedure. In scGAN, we introduce an additional sensitivity parameter w to the generator. The proposed approach effectively parameterizes the loss of the trained detector. The resulting shadow detector is a single network that can generate shadow maps corresponding to different sensitivity levels, obviating the need for multiple models and a costly training procedure. We evaluate our method on the large-scale SBU and UCF shadow datasets, and observe up to 17% error reduction with respect to the previous state-of-the-art method.
CEPR: A Collaborative Exploration and Periodically Returning Model for Location Prediction

With the growing popularity of location-based social networks, numerous location visiting records (e.g., check-ins) continue to accumulate over time. The more these records are collected, the better we can understand users’ mobility patterns and the more accurately we can predict their future locations. However, due to the personality trait of neophilia, people also show propensities of novelty seeking in human mobility, that is, exploring unvisited but tailored locations for them to visit. As such, the existing prediction algorithms, mainly relying on regular mobility patterns, face severe challenges because such behavior is beyond the reach of regularity. As a matter of fact, the prediction of this behavior not only relies on the forecast of novelty-seeking tendency but also depends on how to determine unvisited candidate locations. To this end, we put forward a Collaborative Exploration and Periodically Returning model (CEPR), based on a novel problem, Exploration Prediction (EP), which forecasts whether people will seek unvisited locations to visit, in the following. When people are predicted to do exploration, a state-of-the-art recommendation algorithm, armed with collaborative social knowledge and assisted by geographical influence, will be applied for seeking the suitable candidates; otherwise, a traditional prediction algorithm, incorporating both regularity and the Markov model, will be put into use for figuring out the most possible locations to visit. We then perform case studies on check-ins and evaluate them on two large-scale check-in datasets with 6M and 36M records, respectively. The evaluation results show that EP achieves a roughly 20% classification error rate on both datasets, greatly outperforming the baselines, and that CEPR improves performances by as much as 30% compared to the traditional location prediction algorithms.
Recurrent Neural Network Embedding for Knowledgebase Completion

Knowledge can often be represented using entities connected by relations. For example, the fact that tennis ball is round can be represented as “TennisBall HasShape Round”, where a “TennisBall” is one entity, “HasShape” is a relation and “Round” is another entity. A knowledge base is a way to store such structured information, a knowledge base stores triples of the “an entity-relation-an entity” form, and a real world knowledge base often has millions or billions of such triples. There are several well-known knowledge bases including FreeBase , WordNet , YAGO , etc. They are important in fields like reasoning and question answering; for instance if one asks “what is the shape of a tennis ball”, we can search the knowledge base for the triple as “TennisBall HasShape Round” and output “round” as the answer.
A case study on propagating and updating provenance information using the CIDOC CRM

Provenance information of digital objects maintained by digital libraries and archives is crucial for authenticity assessment, reproducibility and accountability. Such information is commonly stored on metadata placed in various Metadata Repositories (MRs) or Knowledge Bases (KBs). Nevertheless, in various settings it is prohibitive to store the provenance of each digital object due to the high storage space requirements that are needed for having complete provenance. In this paper, we introduce provenance-based inference rules as a means to complete the provenance information, to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). Roughly, we show how provenance information can be propagated by identifying a number of basic inference rules over a core conceptual model for representing provenance. The propagation of provenance concerns fundamental modelling concepts such as actors, activities, events, devices and information objects, and their associations. However, since a MR/KB is not static but changes over time due to several factors, the question that arises is how we can satisfy update requests while still supporting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required add/delete operations, consider two different semantics for deletion of information, and provide the corresponding update algorithms. Finally, we report extensive comparative results for different repository policies regarding the derivation of new knowledge, in datasets containing up to one million RDF triples. The results allow us to understand the tradeoffs related to the use of inference rules on storage space and performance of queries and updates.
Classification of basic footwork in fencing using accelerometer

Analysis and recognition of motion patterns from data acquired by body-worn inertial sensors is an emerging technology in sports. In this paper we propose an effective method for recognition of fencing footwork using a single body-worn accelerometer. We present a challenging dataset consisting of six actions, which were performed by ten persons and repeated ten times by each of them. We propose a segment-based SVM for time-series classification together with a set of informative features. We demonstrate that the method is competitive with 1-NN DTW in terms of classification accuracy. The proposed method achieves classification accuracy slightly better than 70% on the fencing footwork dataset.
Cross-Cultural Pragmatic Failure

/ have given the term pragmatic failure' to the inability to understand 'what is meant by what is said'. In this paper I argue that pragmatic failure is an area of cross-cultural communication breakdown which has received very little attention from language teachers. I suggest that there is one area of pragmatic failure ('pragmalinguistic failure') which is fairly easy to overcome. It is simply a question of highly conventionalized usage which can be taught quite straightforwardly as 'part of the grammar'. The second area ('sociopragmatic failure') is much more difficult to deal with, since it involves the student's system of beliefs as much as his/her knowledge of the language. I argue that it is essential to avoid prescriptivism in this very sensitive area of language in use. To do so we must draw on insights from theoretical pragmatics and develop ways of heightening and refining students' metapragmatic awareness, so that they are able to express themselves as they choose.