nils-herrmann/clean_scirepeval huggingface dataset:
Embedding Entities and Relations for Learning and Inference in Knowledge Bases

We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as BornInCitypa, bq ^ CityInCountrypb, cq ùñ Nationalitypa, cq. We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics, and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-ofthe-art confidence-based rule mining approach in mining horn rules that involve compositional reasoning.
Deep Object Co-Segmentation

This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.
Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video

In this article, we present a method to perform automatic player trajectories mapping based on player detection, unsupervised labeling, efficient multi-object tracking, and playfield registration in broadcast soccer videos. Player detector determines the players' positions and scales by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We first learn the dominant color with accumulate color histogram at the beginning of processing, then use the player detector to collect hundreds of player samples, and learn player appearance codebook by unsupervised clustering. In a soccer game, a player can be labeled as one of four categories: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manual initialization. With the dominant color and player appearance model, we can locate and label each player. After that, we perform multi-object tracking by using Markov Chain Monte Carlo (MCMC) data association to generate player trajectories. Some data driven dynamics are proposed to improve the Markov chain's efficiency, such as label consistency, motion consistency, and track length, etc. Finally, we extract key-points and find the mapping from an image plane to the standard field model, and then map players' position and trajectories to the field. A large quantity of experimental results on FIFA World Cup 2006 videos demonstrate that this method can reach high detection and labeling precision, reliably tracking in scenes of player occlusion, moderate camera motion and pose variation, and yield promising field registration results.
Private Database Queries Using Somewhat Homomorphic Encryption

In a private database query system a client issues queries to a database server and obtains the results without learning anything else about the database and without the server learning the query. In this work we develop tools for implementing private database queries using somewhat-homomorphic encryption (SWHE), that is, using an encryption system that supports only limited computations on encrypted data. We show that a polynomial encoding of the database enables an efficient implementation of several different query types using only low-degree computations on ciphertexts. Specifically, we study two separate settings that offer different privacy/efficiency tradeoffs. In the basic client-server setting, we show that additive homomorphisms are sufficient to implement conjunction and threshold queries. We obtain further efficiency improvements using an additive system that also supports a single homomorphic multiplication on ciphertexts. This implementation hides all aspects of the client’s query from the server, and reveals nothing to the client on non-matching records. To improve performance further we turn to the “Isolated-Box” architecture of De Cristofaro et al. In that architecture the role of the database server is split between two non-colluding parties. The server encrypts and pre-processes the n-record database and also prepares an encrypted inverted index. The server sends the encrypted database and inverted index to a proxy, but keeps the decryption keys to itself. The client interacts with both server and proxy for every query and privacy holds as long as the server and proxy do not collude. We show that using a system that supports only log(n) multiplications on encrypted data it is possible to implement conjunctions and threshold queries efficiently. We implemented our protocols for the Isolated-box architecture using the somewhat homomorphic encryption system by Brakerski, and compared it to a simpler implementation that only uses Paillier’s additively homomorphic encryption system. The implementation using somewhat homomorphic encryption was able to handle a query with a few thousand matches out of a million-record database in just a few minutes, far outperforming the implementation using additively homomorphic encryption.
Convex Subspace Representation Learning from Multi-View Data

Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual norms, we then show a convex min-max dual formulation with a sparsity inducing trace norm can be obtained. We develop a proximal bundle optimization algorithm to globally solve the minmax optimization problem. Our empirical study shows the proposed subspace representation learning method can effectively facilitate multi-view clustering and induce superior clustering results than alternative multiview clustering methods.
Deep Learning Approach for Car Detection in UAV Imagery

This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres) and the extremely high level of detail, which require suitable automatic analysis methods. Our proposed method begins by segmenting the input image into small homogeneous regions, which can be used as candidate locations for car detection. Next, a window is extracted around each region, and deep learning is used to mine highly descriptive features from these windows. We use a deep convolutional neural network (CNN) system that is already pre-trained on huge auxiliary data as a feature extraction tool, combined with a linear support vector machine (SVM) classifier to classify regions into “car” and “no-car” classes. The final step is devoted to a fine-tuning procedure which performs morphological dilation to smooth the detected regions and fill any holes. In addition, small isolated regions are analysed further using a few sliding rectangular windows to locate cars more accurately and remove false positives. To evaluate our method, experiments were conducted on a challenging set of real UAV images acquired over an urban area. The experimental results have proven that the proposed method outperforms the state-of-the-art methods, both in terms of accuracy and computational time.
Extending generalized hough transform to detect 3D objects in laser range data

Automated detection and 3D modelling of objects in laser range data is of great importance in many app lications. Existing approaches to object detection in range data are li mited to either 2.5D data (e.g. range images) or si mple objects with a parametric form (e.g. spheres). This paper describes a new app ro ch to the detection of 3D objects with arbitrary shapes in a point cloud. We present an extension of the generalized Hough trans form to 3D data, which can be used to detect instan ces of an object model in laser range data, independent of the scale and orientatio of the object. We also discuss the computational complexity of the method and provide cost-reduction strategies that can be emplo yed to improve the efficiency of the method.
A Survey of Text Similarity Approaches

Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities. Furthermore, samples of combination between these similarities are presented.
Self-interruptions in discretionary multitasking

Human multitasking is often the result of self-init iated interruptions in the performance of an ongoing task. These self-interruptions occur in the absence of external triggers such as electronic alerts or email notifications. Compared to externally induced interruptions, self-interr uptions have not received enough research attention. To address this gap, this paper develops a typology of self-interruptions based on the integration of Flow Theory and Self-regulation Theory . In this new typology, the two major categories stem from positive and negative feelings of task progress and prospects of goal attainment. The proposed classification is validated in an experimental multitasking environmen t with pre-defined tasks. Empirical findings indicate that negat ive feelings trigger more self-interruptions than positive feelings. In general, more self-interr uptions result in lower accuracy in all tasks. The results suggest that negative internal triggers of self-interruptions unleash a downward spiral that may degrade performance.
Community health workers and the response to HIV/AIDS in South Africa: tensions and prospects.

After a decline in enthusiasm for national community health worker (CHW) programmes in the 1980s, these have re-emerged globally, particularly in the context of HIV. This paper examines the case of South Africa, where there has been rapid growth of a range of lay workers (home-based carers, lay counsellors, DOT supporters etc.) principally in response to an expansion in budgets and programmes for HIV, most recently the rollout of antiretroviral therapy (ART). In 2004, the term community health worker was introduced as the umbrella concept for all the community/lay workers in the health sector, and a national CHW Policy Framework was adopted. We summarize the key features of the emerging national CHW programme in South Africa, which include amongst others, their integration into a national public works programme and the use of non-governmental organizations as intermediaries. We then report on experiences in one Province, Free State. Over a period of 2 years (2004--06), we made serial visits on three occasions to the first 16 primary health care facilities in this Province providing comprehensive HIV services, including ART. At each of these visits, we did inventories of CHW numbers and training, and on two occasions conducted facility-based group interviews with CHWs (involving a total of 231 and 182 participants, respectively). We also interviewed clinic nurses tasked with supervising CHWs. From this evaluation we concluded that there is a significant CHW presence in the South African health system. This infrastructure, however, shares many of the managerial challenges (stability, recognition, volunteer vs. worker, relationships with professionals) associated with previous national CHW programmes, and we discuss prospects for sustainability in the light of the new policy context.
OntoEdit: Collaborative Ontology Development for the Semantic Web

Ontologies now play an important role for enabling the semantic web. They provide a source of precisely defined terms e.g. for knowledge-intensive applications. The terms are used for concise communication across people and applications. Typically the development of ontologies involves collaborative efforts of multiple persons. OntoEdit is an ontology editor that integrates numerous aspects of ontology engineering. This paper focuses on collaborative development of ontologies with OntoEdit which is guided by a comprehensive methodology.
1 Ontology Learning

Learning greatly facilitates the construction of ontologies by the ontology engineer. The notion of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured and semi-structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, and refinement, giving the ontology engineer a wealth of coordinated tools for ontology modelling. Besides of the general architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.
A Hierarchical Latent Structure for Variational Conversation Modeling

Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.
Big Data Semantics in Industry 4.0

The Industry 4.0 is a vision that includes connecting more intensively physical systems with their virtual counterparts in computers. This computerization of manufacturing will bring many advantages, including allowing data gathering, integration and analysis in the scale not seen earlier. In this paper we describe our Semantic Big Data Historian that is intended to handle large volumes of heterogeneous data gathered from distributed data sources. We describe the approach and implementation with a special focus on using Semantic Web technologies for integrating the data.
Fabrication of matrix-addressable InGaN-based microdisplays of high array density

We describe the fabrication and characterization of matrix-addressable microlight-emitting diode (micro-LED) arrays based on InGaN, having elemental diameter of 20 /spl mu/m and array size of up to 128 /spl times/ 96 elements. The introduction of a planar topology prior to contact metallization is an important processing step in advancing the performance of these devices. Planarization is achieved by chemical-mechanical polishing of the SiO/sub 2/-deposited surface. In this way, the need for a single contact pad for each individual element can be eliminated. The resulting significant simplification in the addressing of the pixels opens the way to scaling to devices with large numbers of elements. Compared to conventional broad-area LEDs, the micrometer-scale devices exhibit superior light output and current handling capabilities, making them excellent candidates for a range of uses including high-efficiency and robust microdisplays.
Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function

Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.
Functional equivalence between radial basis function networks and fuzzy inference systems

It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent.
Can Your Eyes Tell Me How You Think? A Gaze Directed Estimation of the Mental Activity

Many applications pointed to the informative potential of the human eyes. In this paper we investigate the possibility of estimating the cognitive process used by a person when addressing a mental challenge, according to the Eye Accessing Cue (EAC) model from the Neuro-Linguistic Programming (NLP) theory [3]. This model states that there is a subtle, yet firm, connection between the non-visual gaze direction and the mental representation system used. From the point of view of computer vision, this work deals with gaze estimation under passive illumination. Using a multistage fusion approach, we show that it is possible to achieve highly accurate results in both terms of eye gaze localization or EAC case recognition.
The Effectiveness of Software Development Technical Reviews: A Behaviorally Motivated Program of Research

ÐSoftware engineers use a number of different types of software development technical review (SDTR) for the purpose of detecting defects in software products. This paper applies the behavioral theory of group performance to explain the outcomes of software reviews. A program of empirical research is developed, including propositions to both explain review performance and identify ways of improving review performance based on the specific strengths of individuals and groups. Its contributions are to clarify our understanding of what drives defect detection performance in SDTRs and to set an agenda for future research. In identifying individuals' task expertise as the primary driver of review performance, the research program suggests specific points of leverage for substantially improving review performance. It points to the importance of understanding software reading expertise and implies the need for a reconsideration of existing approaches to managing reviews. Index TermsÐInspections, walkthroughs, technical reviews, defects, defect detection, groups, group process, group size, expertise, reading, training, behavioral research, theory, research program.
Interventions to facilitate social interaction for young children with autism: review of available research and recommendations for educational intervention and future research.

The purpose of this paper is to review the knowledge available from aggregated research (primarily through 2000) on the characteristics of social interactions and social relationships among young children with autism, with special attention to strategies and tactics that promote competence or improved performance in this area. In its commissioning letter for the initial version of this paper, the Committee on Educational Interventions for Children with Autism of the National Research Council requested "a critical, scholarly review of the empirical research on interventions to facilitate the social interactions of children with autism, considering adult-child interactions (where information is available) as well as child-child interactions, and including treatment of [one specific question]: What is the empirical evidence that social irregularities of children with autism are amenable to remediation?" To do this, the paper (a) reviews the extent and quality of empirical literature on social interaction for young children with autism; (b) reviews existing descriptive and experimental research that may inform us of relations between autism and characteristics that support social development, and efforts to promote improved social outcomes (including claims for effectiveness for several specific types of intervention); (c) highlights some possible directions for future research; and (d) summarizes recommendations for educational practices that can be drawn from this research.